System and method for expressive language, developmental disorder, and emotion assessment

ABSTRACT

In one embodiment, a method for detecting autism in a natural language environment using a microphone, sound recorder, and a computer programmed with software for the specialized purpose of processing recordings captured by the microphone and sound recorder combination, the computer programmed to execute the method, includes segmenting an audio signal captured by the microphone and sound recorder combination using the computer programmed for the specialized purpose into a plurality recording segments. The method further includes determining which of the plurality of recording segments correspond to a key child. The method further includes determining which of the plurality of recording segments that correspond to the key child are classified as key child recordings. Additionally, the method includes extracting phone-based features of the key child recordings; comparing the phone-based features of the key child recordings to known phone-based features for children; and determining a likelihood of autism based on the comparing.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 12/395,567 entitled “System And Method For Expressive Language,Developmental Disorder, And Emotion Assessment” filed on Feb. 27, 2009,which is a continuation-in-part of U.S. patent application Ser. No.12/359,124 entitled “System And Method For Expressive Language,Developmental Disorder, And Emotion Assessment” filed on Jan. 23, 2009,which is a continuation-in-part of U.S. patent application Ser. No.12/109,785 entitled “System And Method For Expressive LanguageDevelopment” filed on Apr. 25, 2008, now U.S. Pat. No. 8,744,847 issuedJun. 3, 2014, which is a continuation-in-part of U.S. patent applicationSer. No. 12/018,647 entitled “System And Method For Detection AndAnalysis Of Speech” filed Jan. 23, 2008, now U.S. Pat. No. 8,078,465issued Dec. 13, 2011, which claims priority to U.S. Provisional PatentApplication No. 60/886,122 entitled “System And Method For Detection AndAnalysis Of Speech” filed Jan. 23, 2007, and U.S. Provisional PatentApplication No. 60/886,167 entitled “Pocket For Positioning A VoiceRecorder” filed Jan. 23, 2007, all of which are incorporated herein byreference. This application is also a continuation-in-part of U.S.patent application Ser. No. 11/162,520, now U.S. Pat. No. 8,708,702issued Apr. 29, 2014, entitled “Systems And Methods For Learning UsingContextual Feedback” filed Sep. 13, 2005 (the “'520 Application”), whichclaims priority to U.S. Provisional Application No. 60/522,340 filedSep. 16, 2004, both of which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to automated language assessmentand, specifically, to assessing a key child's expressive languagedevelopment by analyzing phones, phone-like sounds, and protophones usedby the child, to analyzing recordings to assist in the detection ofdiseases and disorders such as Autism, and to detecting emotion.

BACKGROUND OF THE INVENTION

As discussed in more detail in the '520 Application, the languageenvironment surrounding a young child is key to the child's development.A child's language and vocabulary ability at age three, for example, canindicate intelligence and test scores in academic subjects such asreading and math at later ages. Improving language ability typicallyresults in a higher intelligent quotient (IQ) as well as improvedliteracy and academic skills.

Exposure to a rich aural or listening language environment in which manywords are spoken with a large number of interactive conversational turnsbetween the child and adult and a relatively high number of affirmationsversus prohibitions may promote an increase in the child's languageability and IQ. The effect of a language environment surrounding a childof a young age on the child's language ability and IQ may beparticularly pronounced.

In the first four years of human life, a child experiences a highlyintensive period of speech and language development due in part to thedevelopment and maturing of the child's brain. Even after children beginattending school or reading, much of the child's language ability andvocabulary, including the words known (receptive vocabulary) and thewords the child uses in speech (expressive vocabulary), are developedfrom conversations the child experiences with other people.

In addition to hearing others speak to them and responding (i.e.,conversational turns), a child's language development may be promoted bythe child's own speech. The child's own speech is a dynamic indicator ofcognitive functioning, particularly in the early years of a child'slife. Research techniques have been developed which involve counting ayoung child's vocalizations and utterances and length of utterances toestimate a child's cognitive development. Current processes ofcollecting information may include obtaining data via a human observerand/or a transcription of an audio recording of the child's speech. Thedata is analyzed to provide metrics with which the child's languageenvironment can be analyzed and potentially modified to promoteincreasing the child's language development and IQ.

The presence of a human observer, however, may be intrusive, influentialon the child's performance, costly, and unable to adequately obtaininformation on a child's natural environment and development.Furthermore, the use of audio recordings and transcriptions is a costlyand time-consuming process of obtaining data associated with a child'slanguage environment. The analysis of such data to identify canonicalbabbling, count the number of words, determine mean length of utterancesand other vocalization metrics, and determine content spoken is alsotime intensive.

Counting the number of words and determining content spoken may beparticularly time and resource intensive, even for electronic analysissystems, since each word is identified along with its meaning.Accordingly, a need exists for methods and systems for obtaining andanalyzing data associated with a child's language environmentindependent of content and reporting metrics based on the data in atimely manner. The analysis should also include an automatic assessmentof the child's expressive language development.

Beyond an automatic assessment of a child's expressive languagedevelopment, a need exists for the development of specific metrics andmethodologies for determining specific developmental disorders in achild. As expressed above, a test that is largely non-invasive, in termsof providing a human observer, and that is of low cost while at the sametime generating a substantial amount of data is desirable. One suchdevelopmental disorder of interest that can be detected through theanalysis of speech is autism. Another factor contributing to languagedevelopment may be emotion. When children are exposed to an emotionallystressed environment there learning and language development may suffer.Therefore, a system and method for detecting the emotional content ofsubject interactions may be desirable for assisting in languagedevelopment.

SUMMARY

Certain embodiments of the system and method for expressive languagedevelopment provide methods and systems for providing metrics associatedwith a key child's language environment and development in a relativelyquick and cost effective manner. The metrics may be used to promoteimprovement of the language environment, key child's languagedevelopment, and/or to track development of the child's language skills.In one embodiment of the present invention, a method is provided forgenerating metrics associated with the key child's language environment.An audio recording from the language environment can be captured. Theaudio recordings may be segmented into a plurality of segments. Asegment ID can be identified for each of the plurality of segments. Thesegment ID may identify a source for audio in the segment of therecording. Key child segments can be identified from the segments. Eachof the key child segments may have the key child as the segment ID. Keychild segment characteristics can be estimated based in part on at leastone of the key child segments. The key child segment characteristics canbe estimated independent of content of the key child segments. At leastone metric associated with the language environment and/or languagedevelopment may be determined using the key child segmentcharacteristics. Examples of metrics include the number of words orvocalizations spoken by the key child in a pre-set time period and thenumber of conversational turns. The at least one metric can be output toan output device.

In some embodiments, adult segments can be identified from the segments.Each of the adult segments may have the adult as the segment ID. Adultsegment characteristics can be estimated based in part on at least oneof the adult segments. The adult segment characteristics can beestimated independent of content of the adult segments. At least onemetric associated with the language environment may be determined usingthe adult segment characteristics.

In one embodiment of the system and method for expressive languagedevelopment, a system for providing metrics associated with a keychild's language environment is provided. The system may include arecorder and a processor-based device. The recorder may be adapted tocapture audio recordings from the language environment and provide theaudio recordings to a processor-based device. The processor-based devicemay include an application having an audio engine adapted to segment theaudio recording into segments and identify a segment ID for each of thesegments. At least one of the segments may be associated with a keychild segment ID. The audio engine may be further adapted to estimatekey child segment characteristics based in part on the at least one ofthe segments, determine at least one metric associated with the languageenvironment or language development using the key child segmentcharacteristics, and output the at least one metric to an output device.The audio engine may estimate the key child segment characteristicsindependent of content of the segments.

In one embodiment of the system and method for expressive languagedevelopment, the key child's vocalizations are analyzed to identify thenumber of occurrences of certain phones, phone-like sounds, andprotophones and to calculate a frequency distribution or a durationdistribution for the phones, phone-like sounds, and protophones. Theanalysis may be performed independent of the content of thevocalizations. A phone decoder designed for use with an automatic speechrecognition system used to identify content from adult speech can beused to identify the phones, phone-like sounds, and protophones. The keychild's chronological age is used to select an age-based model whichuses the distribution of the phones, phone-like sounds, and protophones,as well as age-based weights associated with each phone, phone-likesound, and protophone, to assess the key child's expressive languagedevelopment. The assessment can result in a standard score, an estimateddevelopmental age, or an estimated mean length of utterance measure.

In one embodiment, a method of assessing a key child's expressivelanguage development includes processing an audio recording taken in thekey child's language environment to identify segments of the recordingthat correspond to the key child's vocalizations. The method furtherincludes applying an adult automatic speech recognition phone decoder tothe segments to identify each occurrence of each of a plurality ofbi-phone categories. Each of the bi-phone categories corresponds to apre-defined speech sound sequence. The method also includes determininga distribution for the bi-phone categories and using the distribution inan age-based model to assess the key child's expressive languagedevelopment.

In another embodiment, a system for assessing a key child's languagedevelopment includes a processor-based device comprising an applicationhaving an audio engine for processing an audio recording taken in thekey child's language environment to identify segments of the recordingthat correspond to the key child's vocalizations. The system alsoincludes an adult automatic speech recognition phone decoder forprocessing the segments that correspond to the key child's vocalizationsto identify each occurrence of each of a plurality of bi-phonecategories. Each of the bi-phone categories corresponds to a pre-definedspeech sound sequence. The system further includes an expressivelanguage assessment component for determining a distribution for thebi-phone categories and using the distributions in an age-based model toassess the key child's expressive language development. The age-basedmodel is selected based on the key child's chronological age, and theage-based model includes a weight associated with each of the bi-phonecategories.

In one embodiment of the system and method for expressive languagedevelopment, a method for detecting autism in a natural languageenvironment includes using a microphone, sound recorder, and a computerprogrammed with software for the specialized purpose of processingrecordings captured by the microphone and sound recorder combination.The computer is programmed to execute a method that includes segmentingan audio signal captured by the microphone and sound recordercombination using the computer programmed for the specialized purposeinto a plurality of recording segments. The method further includesdetermining which of the plurality of recording segments correspond to akey child. The method also includes extracting acoustic parameters ofthe key child recordings and comparing the acoustic parameters of thekey child recordings to known acoustic parameters for children. Themethod returns a determination of a likelihood of autism.

In another embodiment, a method for detecting autism includestransforming an audio recording to display an indication of autism on anoutput mechanism selected from the group consisting of a display, aprintout, and an audio output, the transforming of the audio recordingperformed by comparing it to a model developed by analyzing thetransparent parameters of a plurality of sound recordings captured in anatural language environment.

Additionally, another embodiment includes a method for detecting adisorder in a natural language environment using a microphone, soundrecorder, and a computer programmed with software for the specializedpurpose of processing recordings captured by the microphone and soundrecorder combination. The computer is programmed to execute a method.The method includes segmenting an audio signal captured by themicrophone and sound recorder combination using the computer programmedfor the specialized purpose into a plurality of recording segments;determining which of the plurality of recording segments correspond to akey subject; determining which of the plurality of recording segmentsthat correspond to the key subject are classified as key subjectrecordings; extracting acoustic parameters of the key subjectrecordings; comparing the acoustic parameters of the key subjectrecordings to known acoustic parameters for subjects; and determining alikelihood of the disorder.

In yet another embodiment, a method for detecting a disorder includestransforming an audio recording to display an indication of autism on anoutput mechanism selected from the group consisting of a display, aprintout, and an audio output, the transforming of the audio recordingperformed by comparing it to a model developed by analyzing thetransparent parameters of a plurality of sound recordings captured in anatural language environment. In the case of each of the plurality ofsound recordings, the analyzing includes segmenting the sound recordinginto a plurality of recording segments, wherein the sound recording iscaptured by a microphone and sound recorder combination; determiningwhich of the plurality of recording segments correspond to a keysubject; determining which of the plurality of recording segments thatcorrespond to the key subject are classified as key subject recordings;and extracting acoustic parameters of the key subject recordings.

In one embodiment, a method of creating an automatic languagecharacteristic recognition system includes receiving a plurality ofaudio recordings. The audio recordings are segmented to create aplurality of audio segments for each audio recording. The plurality ofaudio segments is clustered according to audio characteristics of eachaudio segment to form a plurality of audio segment clusters.

In one embodiment, a method of decoding speech using an automaticlanguage characteristic recognition system includes receiving aplurality of audio recordings and segmenting each of the plurality ofaudio recordings to create a first plurality of audio segments for eachaudio recording. The method further includes clustering each audiosegment of the plurality of audio recordings according to audiocharacteristics of each audio segment to form a plurality of audiosegment clusters. The method additionally includes receiving a new audiorecording, segmenting the new audio recording to create a secondplurality of audio segments for the new audio recording, and determiningto which cluster of the plurality of audio segment clusters each segmentof the second plurality of audio segments corresponds.

In one embodiment, a method of determining the emotion of an utteranceincludes receiving the utterance at a processor-based device comprisingan application having an audio engine. The method further includesextracting emotion-related acoustic features from the utterance. Themethod additionally includes comparing the emotion-related acousticfeatures to a plurality of models representative of emotions. Furtherincluded is selecting a model from the plurality of models based on thecomparing and outputting the emotion corresponding to the selectedmodel.

In one embodiment, a method for detecting autism in a natural languageenvironment using a microphone, sound recorder, and a computerprogrammed with software for the specialized purpose of processingrecordings captured by the microphone and sound recorder combination,the computer programmed to execute the method, includes segmenting anaudio signal captured by the microphone and sound recorder combinationusing the computer programmed for the specialized purpose into aplurality recording segments. The method further includes determiningwhich of the plurality of recording segments correspond to a key child.The method further includes determining which of the plurality ofrecording segments that correspond to the key child are classified askey child recordings. Additionally, the method includes extractingphone-based features of the key child recordings; comparing thephone-based features of the key child recordings to known phone-basedfeatures for children; and determining a likelihood of autism based onthe comparing. In one alternative, the comparing includes LogicalRegression Analysis. In another alternative, the comparing includesLinear Discriminate Analysis. In one alternative, the method furtherincludes transforming a display of a user to display the likelihood ofautism. In another alternative, the method further includes transformingan information storage device to store the likelihood of autism.Additionally, the phone-based features may be represented by a pluralityof feature vectors. Additionally, the comparing may include that theplurality of feature vectors are compared to the known phone-basedfeatures for children to return a plurality of results, wherein there isa result of the plurality of results for each of the plurality offeature vectors, and the plurality of results are averaged for use inthe determining. Additionally, the plurality of feature vectors may beaveraged to a single feature vector for use in the comparing.

In one embodiment, a method includes capturing an audio recording from alanguage environment of a key child and segmenting the audio recordinginto a plurality of segments. The method further includes identifying asegment ID for each of the plurality of segments, the segment IDidentifying a source for audio in the segment. The identifying includescomparing the plurality of segments to a plurality of models, a model ofthe plurality of models includes a key child model and the identifyingincludes identifying a plurality of key child segments from theplurality of segments. The method further includes estimating key childsegment characteristics based in part on at least one of the pluralityof key child segments. The key child segment characteristics areestimated independent of content of the plurality of key child segments.The content is the meaning of the plurality of key child segments. Themethod further includes determining at least one metric associated withthe language environment using the key child segment characteristics andoutputting the at least one metric. Optionally, the plurality of modelsfurther include models for other children, male adults, female adults,noise, and TV noise. Alternatively, the plurality of models furtherinclude an adult model that includes characteristics of sounds from anadult, an electronic device model that includes characteristics ofsounds from an electronic device, a noise model that includescharacteristics of sounds attributable to noise, an other child modelthat includes characteristics of sounds from a child other than the keychild, a parentese model that includes complexity level speech criteriaof adult sounds, an age-dependent key child model that includescharacteristics of sounds of a key child of different ages, and aloudness/clearness detection model that includes characteristics ofsounds directed to a key child. In one configuration, a maximumlikelihood analysis is used to perform the segmenting and identifying.

In one embodiment, a method includes capturing an audio recording from alanguage environment of a key child. The method further includessegmenting the audio recording and simultaneously and identifying asegment ID for each of a plurality of segments segmented from the audiorecording, the segment ID identifying a source for audio in the segment,the identifying includes comparing the plurality of segments to aplurality of models. The method further includes determining at leastone metric associated with the language environment based on theplurality of segments that have been identified and outputting the atleast one metric. Optionally, a maximum likelihood analysis is used toperform the segmenting and identifying. Alternatively, a model of theplurality of models includes a key child model and the identifyingincludes identifying a plurality of key child segments from theplurality of segments. In one configuration, the determining includesusing the key child segment characteristics to determine the at leastone metric. Optionally, the method further includes estimating key childsegment characteristics based in part on at least one of the pluralityof key child segments, the key child segment characteristics areestimated independent of content of the plurality of key child segments,the content is the meaning of the plurality of key child segments.Alternatively, a Minimum Duration Gaussian Mixture Model (MD-GMM) isused to perform the segmenting and identifying.

These embodiments are mentioned not to limit or define the invention,but to provide examples of embodiments of the invention to aidunderstanding thereof. Embodiments are discussed in the DetailedDescription and advantages offered by various embodiments of the presentinvention may be further understood by examining the DetailedDescription and Drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentinvention are better understood when the following Detailed Descriptionis read with reference to the accompanying drawings, wherein:

FIG. 1 illustrates a key child's language environment according to oneembodiment of the present invention;

FIG. 2a is a front view of a recorder in a pocket according to oneembodiment of the present invention;

FIG. 2b is a side view of the recorder and pocket of FIG. 2 a;

FIG. 3 is a recording processing system according to one embodiment ofthe present invention;

FIG. 4 is flow chart of a method for processing recordings according toone embodiment of the present invention;

FIG. 5 is a flow chart of a method for performing further recordingprocessing according to one embodiment of the present invention;

FIG. 6 illustrates sound energy in a segment according to one embodimentof the present invention;

FIGS. 7-12 are screen shots illustrating metrics provided to an outputdevice according to one embodiment of the present invention;

FIG. 13 illustrates the correlation between chronological age andcertain phones;

FIG. 14 illustrates the non-linear relationship between some of thephones of FIG. 13 and chronological age;

FIGS. 15a and 15b , collectively referred to herein as FIG. 15, is atable illustrating the weights used for the expressive language indexz-score according to one embodiment of the present invention;

FIG. 16 is a block diagram illustrating the system used to assesslanguage development according to one embodiment of the presentinvention;

FIG. 17 is a block diagram illustrating one embodiment of a method usedto detect disorders or diseases in individuals;

FIG. 18 is a block diagram illustrating one embodiment of a method usedto create trends for a population of normal individuals and individualswith the disorder or disease in question;

FIG. 19 shows an acoustic signal captured and transformed into agraphical representation;

FIG. 20 shows a graphical representation of an empirical display for howthe grouping of formant bandwidths can mark the articulation level;

FIG. 21 shows 12 acoustic parameters of vocal development;

FIG. 22 shows an example of non-acoustic parameters;

FIG. 23 shows a data set used in developing a model for the detection ofautism;

FIG. 24 shows a trend chart for acoustic parameters in autistic andnormally developing children;

FIG. 25 shows a trend chart for acoustic parameters in autistic,normally developing, and language delayed children;

FIG. 26 shows a trend chart for acoustic parameters in normallydeveloping and language delayed children;

FIG. 27 shows non-acoustic parameters in normal and autistic children;

FIG. 28 shows a trend chart for acoustic parameters in autistic,normally developing, and language delayed children;

FIG. 29 shows a trend chart for acoustic parameters in normallydeveloping and language delayed children;

FIG. 30 shows the results of the use of logistical regression analysisin determining normally developing and autistic individuals;

FIG. 31 shows tables showing the accuracy of a machine employing oneembodiment of the system and methods of detecting autism;

FIG. 32 shows an illustration of K-means clusters;

FIG. 33 shows a methodology for determining emotion in an utterance;

FIG. 34 shows a graph of detection rate vs. posterior probability cutoffthreshold for detection for combination of vectors before analysis; and

FIG. 35 shows a graph of detection rate vs. posterior probability cutoffthreshold for detection for analysis of vectors before combination.

DETAILED DESCRIPTION

Certain aspects and embodiments of the present invention are directed tosystems and methods for monitoring and analyzing the languageenvironment, vocalizations, and the development of a key child. A keychild as used herein may be a child, an adult, such as an adult withdevelopmental disabilities, or any individual whose language developmentis of interest. A key child's language environment and languagedevelopment can be monitored without placing artificial limitations onthe key child's activities or requiring a third-party observer. Thelanguage environment can be analyzed to identify words or other noisesdirected to or vocalized by the key child independent of content.Content may include the meaning of vocalizations such as words andutterances. The analysis can include the number of responses between thechild and another, such as an adult (referred to herein as“conversational turns”), and the number of words spoken by the childand/or another, independent of content of the speech.

A language environment can include a natural language environment orother environments such as a clinical or research environment. A naturallanguage environment can include an area surrounding a key child duringhis or her normal daily activities and contain sources of sounds thatmay include the key child, other children, an adult, an electronicdevice, and background noise. A clinical or research environment caninclude a controlled environment or location that contains pre-selectedor natural sources of sounds.

In some embodiments of the present invention, the key child may wear anarticle of clothing that includes a recording device located in a pocketattached to or integrated with the article of clothing. The recordingdevice may be configured to record and store audio associated with thechild's language environment for a predetermined amount of time. Theaudio recordings can include noise, silence, the key child's spokenwords or other sounds, words spoken by others, sounds from electronicdevices such as televisions and radios, or any sound or words from anysource. The location of the recording device preferably allows it torecord the key child's words and noises and conversational turnsinvolving the key child without interfering in the key child's normalactivities. During or after the pre-set amount of time, the audiorecordings stored on the recording device can be analyzed independent ofcontent to provide characteristics associated with the key child'slanguage environment or language development. For example, therecordings may be analyzed to identify segments and assign a segment IDor a source for each audio segment using a Minimum Duration GaussianMixture Model (MD-GMM).

Sources for each audio segment can include the key child, an adult,another child, an electronic device, or any person or object capable ofproducing sounds. Sources may also include general sources that are notassociated with a particular person or device. Examples of such generalsources include noise, silence, and overlapping sounds. In someembodiments, sources are identified by analyzing each audio segmentusing models of different types of sources. The models may include audiocharacteristics commonly associated with each source. In someembodiments, to detect the source type of audio signal, silence isdetected. Any non-silent segment may still contain some short silenceperiod, such as the pause involved in the explosive consonants like “p”and “t”. Such a short low energy region may not contain the informationabout the signal source type; thus, it will be removed from thelikelihood calculation of a non-silence-segment. Audio segments forwhich the key child or an adult is identified as the source may befurther analyzed, such as by determining certain characteristicsassociated with the key child and/or adult, to provide metricsassociated with the key child's language environment or languagedevelopment.

In some embodiments of the present invention, the key child is a childbetween the ages of zero and four years old. Sounds generated by youngchildren differ from adult speech in a number of respects. For example,the child may generate a meaningful sound that does not equate to aword; the formant transitions from a consonant to a vowel or visa-versafor child speech are less pronounced than the transitions for adultspeech, and the child's speech changes over the age range of interestdue to physical changes in the child's vocal tract. Differences betweenchild and adult speech may be recognized and used to analyze childspeech and to distinguish child speech from adult speech, such as inidentifying the source for certain audio segments.

Certain embodiments of the present invention use a system that analyzesspeech independent of content rather than a system that uses speechrecognition to determine content. These embodiments greatly reduce theprocessing time of an audio file and require a system that issignificantly less expensive than if a full speech recognition systemwere used. In some embodiments, speech recognition processing may beused to generate metrics of the key child's language environment andlanguage development by analyzing vocalizations independent of content.In one embodiment, the recommended recording time is twelve hours with aminimum time of ten hours. In order to process the recorded speech andto provide meaningful feedback on a timely basis, certain embodiments ofthe present invention are adapted to process a recording at or underhalf of real time. For example, the twelve-hour recording may beprocessed in less than six hours. Thus, the recordings may be processedovernight so that results are available the next morning. Other periodsof recording time may be sufficient for generating metrics associatedwith the key child's language environment and/or language developmentdepending upon the metrics of interest and/or the language environment.A one- to two-hour recording time may be sufficient in somecircumstances such as in a clinical or research environment. Processingfor such recording times may be less than one hour.

Audio Acquisition

As stated above, a recording device may be used to capture, record, andstore audio associated with the key child's language environment andlanguage development. The recording device may be any type of deviceadapted to capture and store audio and to be located in or around achild's language environment. In some embodiments, the recording deviceincludes one or more microphones connected to a storage device andlocated in one or more rooms that the key child often occupies. In otherembodiments, the recording device is located in an article of clothingworn by the child.

FIG. 1 illustrates a key child, such as child 100, in a languageenvironment 102 wearing an article of clothing 104 that includes apocket 106. The pocket 106 may include a recording device (not shown)that is adapted to record audio from the language environment 102. Thelanguage environment 102 may be an area surrounding the child 100 thatincludes sources for audio (not shown), including one or more adults,other children, and/or electronic devices such as a television, a radio,a toy, background noise, or any other source that produces sounds.Examples of language environment 102 include a natural languageenvironment and a clinical or research language environment. The articleof clothing 104 may be a vest over the child's 100 normal clothing, thechild's 100 normal clothing, or any article of clothing commonly worn bythe key child.

In some embodiments, the recorder is placed at or near the center of thekey child's chest. However, other placements are possible. The recordingdevice in pocket 106 may be any device capable of recording audioassociated with the child's language environment.

One example of a recording device is a digital recorder of the LENAsystem. The digital recorder may be relatively small and lightweight andcan be placed in pocket 106. The pocket 106 can hold the recorder inplace in an unobtrusive manner so that the recorder does not distractthe key child, other children, and adults that interact with the keychild. FIGS. 2a and 2b illustrate one embodiment of a pocket 106including a recorder 108. The pocket 106 may be designed to keep therecorder 108 in place and to minimize acoustic interference. The pocket106 can include an inner area 110 formed by a main body 112 and anoverlay 114 connected to the main body 112 via stitches 116 or anotherconnecting mechanism. The main body 112 can be part of the clothing orattached to the article of clothing 104 using stitches or otherwise. Astretch layer 118 may be located in the inner area 110 and attached tothe main body 112 and overlay 114 via stitches 116 or other connectingmechanism. The recorder 108 can be located between the main body 112 andthe stretch layer 118. The stretch layer 118 may be made of a fabricadapted to stretch but provide a force against the recorder 108 toretain the recorder 108 in its position. For example, the stretch layermay be made from a blend of nylon and spandex, such as 85% nylon, 15%spandex, which helps to keep the recorder in place. The overlay 114 maycover the stretch layer 118 and may include at least one opening wherethe microphone of recorder 108 is located. The opening can be coveredwith a material that provides certain desired acoustic properties. Inone embodiment, the material is 100% cotton.

The pocket 106 may also include snap connectors 120 by which the overlay114 is opened and closed to install or remove the recorder 108. In someembodiments, at least one of the stitches 116 can be replaced with azipper to provide access to the recorder 108 in addition or alternativeto using snap connectors 120.

If the recorder 108 includes multiple microphones, then the pocket 106may include multiple openings that correspond to the placement of themicrophones on the recorder 108. The particular dimensions of the pocket106 may change as the design of the recorder 108 changes or as thenumber or type of microphones change. In some embodiments, the pocket106 positions the microphone relative to the key child's mouth toprovide certain acoustical properties and secure the microphone (andoptionally the recorder 108) in a manner that does not result infriction noises. The recorder 108 can be turned on and thereafter recordaudio, including speech by the key child, other children, and adults, aswell as other types of sounds that the child encounters, includingtelevision, toys, environmental noises, etc. The audio may be stored inthe recorder 108. In some embodiments, the recorder can be periodicallyremoved from pocket 106 and the stored audio can be analyzed.

Illustrative Audio Recording Analysis System Implementation

Methods for analyzing audio recordings from a recorder according tovarious embodiments of the present invention may be implemented on avariety of different systems. An example of one such system isillustrated in FIG. 3. The system includes the recorder 108 connected toa processor-based device 200 that includes a processor 202 and acomputer-readable medium, such as memory 204. The recorder 108 may beconnected to the processor-based device 200 via wireline or wirelessly.In some embodiments, the recorder 108 is connected to the device 200 viaa USB cable. The device 200 may be any type of processor-based device,examples of which include a computer and a server. Memory 204 may beadapted to store computer-executable code and data. Computer-executablecode may include an application 206, such as a data analysis applicationthat can be used to view, generate, and output data analysis. Theapplication 206 may include an audio engine 208 that, as described inmore detail below, may be adapted to perform methods according tovarious embodiments of the present invention to analyze audio recordingsand generate metrics associated therewith. In some embodiments, theaudio engine 208 may be a separate application that is executableseparate from, and optionally concurrent with, application 206. Memory204 may also include a data storage 210 that is adapted to store datagenerated by the application 206 or audio engine 208, or input by auser. In some embodiments, data storage 210 may be separate from device200 but connected to the device 200 via wireline or wireless connection.

The device 200 may be in communication with an input device 212 and anoutput device 214. The input device 212 may be adapted to receive userinput and communicate the user input to the device 200. Examples ofinput device 212 include a keyboard, mouse, scanner, and networkconnection. User inputs can include commands that cause the processor202 to execute various functions associated with the application 206 orthe audio engine 208. The output device 214 may be adapted to providedata or visual output from the application 206 or the audio engine 208.In some embodiments, the output device 214 can display a graphical userinterface (GUI) that includes one or more selectable buttons that areassociated with various functions provided by the application 206 or theaudio engine 208. Examples of output device 214 include a monitor,network connection, and printer. The input device 212 may be used tosetup or otherwise configure audio engine 208. For example, the age ofthe key child and other information associated with the key child'slearning environment may be provided to the audio engine 208 and storedin local storage 210 during a setup or configuration.

The audio file stored on the recorder 108 may be uploaded to the device200 and stored in local storage 210. In one embodiment, the audio fileis uploaded in a proprietary format which prevents the playback of thespeech from the device 200 or access to content of the speech, therebypromoting identity protection of the speakers. In other embodiments, theaudio file is uploaded without being encoded to allow for storage inlocal storage 210 and playback of the file or portions of the file.

In some embodiments, the processor-based device 200 is a web server, andthe input device 212 and output device 214 are combined to form acomputer system that sends data to and receives data from the device 200via a network connection. The input device 212 and output device 214 maybe used to access the application 206 and audio engine 208 remotely andcause it to perform various functions according to various embodimentsof the present invention. The recorder 108 may be connected to the inputdevice 212 and output device 214, and the audio files stored on therecorder 108 may be uploaded to the device 200 over a network such as aninternet or intranet where the audio files are processed and metrics areprovided to the output device 214. In some embodiments, the audio filesreceived from a remote input device 212 and output device 214 may bestored in local storage 210 and subsequently accessed for researchpurposes such as on a child's learning environment or otherwise.

To reduce the amount of memory needed on the recorder 108, the audiofile may be compressed. In one embodiment, a DVI-4 ADPCM compressionscheme is used. If a compression scheme is used, then the file isdecompressed after it is uploaded to the device 200 to a normal linearPCM audio format.

Illustrative Methods for Audio Recording Analysis

Various methods according to various embodiments of the presentinvention can be used to analyze audio recordings. FIG. 4 illustratesone embodiment of a method for analyzing and providing metrics based onthe audio recordings from a key child's language environment. Forpurposes of illustration only, the elements of this method are describedwith reference to the system depicted in FIG. 3. Other systemimplementations of the method are possible.

In block 302, the audio engine 208 divides the recording into one ormore audio segments and identifies a segment ID or source for each ofthe audio segments from the recording received from the recorder 108.This process is referred to herein as “segmentation” and “segment ID”.An audio segment may be a portion of the recording having a certainduration and including acoustic features associated with the child'slanguage environment during the duration. The recording may include anumber of audio segments, each associated with a segment ID or source.Sources may be an individual or device that produces the sounds withinthe audio segment. For example, an audio segment may include the soundsproduced by the key child, who is identified as the source for thataudio segment. Sources also can include other children, adults,electronic devices, noise, overlapped sounds, and silence. Electronicdevices may include televisions, radios, telephones, toys, and anydevice that provides recorded or simulated sounds such as human speech.

Sources associated with each of the audio segments may be identified toassist in further classifying and analyzing the recording. Some metricsprovided by some embodiments of the present invention include dataregarding certain sources and disregard data from other sources. Forexample, audio segments associated with live speech directed to the keychild can be distinguished from audio segments associated withelectronic devices, since live speech has been shown to be a betterindicator and better promoter of a child's language development thanexposure to speech from electronic devices.

To perform segmentation to generate the audio segments and identify thesources for each segment, a number of models may be used that correspondto the key child, other children, male adult, female adult, noise, TVnoise, silence, and overlap. Alternative embodiments may use more,fewer, or different models to perform segmentation and identify acorresponding segment ID. One such technique performs segmentation andsegment ID separately. Another technique performs segmentation andidentifies a segment ID for each segment concurrently.

Traditionally, a Hidden Markov Model (HMM) with minimum durationconstraint has been used to perform segmentation and identify segmentIDs concurrently. A number of HMM models may be provided, eachcorresponding to one source. The result of the model may be a sequenceof sources with a likelihood score associated with each source. Theoptimal sequence may be searched using a Viterbi algorithm or dynamicprogramming and the “best” source identified for each segment based onthe score. However, this approach may be complex for some segments inpart because it uses transition probabilities from one segment toanother—i.e., the transition between each segment. Transitionprobabilities are related to duration modeling of each source. HMMduration models may have discrete geometric distribution or continuousexponential distribution, which may not be appropriate for the soundsources of concern. Most recordings may include segments of having ahigh degree of variation in their duration. Although the HMM model maybe used in some embodiments of the present invention, alternativetechniques may be used to perform segmentation and segment ID.

An alternative technique used in some embodiments of the presentinvention to perform segmentation and segment ID is a Minimum DurationGaussian Mixture Model (MD-GMM). Each model of the MD-GMM may includecriteria or characteristics associated with sounds from differentsources. Examples of models of the MD-GMM include a key child model thatincludes characteristics of sounds from a key child, an adult model thatincludes characteristics of sounds from an adult, an electronic devicemodel that includes characteristics of sounds from an electronic device,a noise model that includes characteristics of sounds attributable tonoise, an other child model that includes characteristics of sounds froma child other than the key child, a parentese model that includescomplexity level speech criteria of adult sounds, an age-dependent keychild model that includes characteristics of sounds of a key child ofdifferent ages, and a loudness/Page clearness detection model thatincludes characteristics of sounds directed to a key child. Some modelsinclude additional models. For example, the adult model may include anadult male model that includes characteristics of sounds of an adultmale and an adult female model that includes characteristics of soundsof an adult female. The models may be used to determine the source ofsound in each segment by comparing the sound in each segment to criteriaof each model and determining if a match of a pre-set accuracy existsfor one or more of the models.

In some embodiments of the present invention, the MD-GMM techniquebegins when a recording is converted to a sequence of frames orsegments. Segments having a duration of 2*D, where D is a minimumduration constraint, are identified using a maximum log-likelihoodalgorithm for each type of source. The maximum score for each segment isidentified. The source associated with the maximum score is correlatedto the segment for each identified segment.

The audio engine 208 may process recordings using the maximum likelihoodMD-GMM to perform segmentation and segment ID. The audio engine 208 maysearch all possible segment sequences under a minimum durationconstraint to identify the segment sequence with maximum likelihood. Onepossible advantage of MD-GMM is that any segment longer than twice theminimum duration (2*D) could be equivalently broken down into severalsegments with a duration between the minimum duration (D) and two timesthe minimum duration (2*D), such that the maximum likelihood searchprocess ignores all segments longer than 2*D. This can reduce the searchspace and processing time. The following is an explanation of oneimplementation of using maximum likelihood MD-GMM. Other implementationsare also possible:

-   -   1. Acoustic Feature Extraction—the audio stream is converted to        a stream of feature vectors {X₁, X₂ . . . X_(T)|X_(i)εR^(n)}        using a feature extraction algorithm, such as the MFCC        (mel-frequency cepstrum coefficients).    -   2. Log likelihood calculation for a segment {X₁, X₂ . . .        X_(S)}:

${Lcs} = {\sum\limits_{i = 1}^{s}\;{\log\left( {f_{c}\left( X_{i} \right)} \right)}}$where f_(c)(X₁) is the likelihood of frame X being in class c.

The following describes one procedure of maximum likelihood MD-GMMsearch:

-   -   3. Initialize searching variables: S(c,0,0)=0, c=1, . . . , C,        where c is the index for all segment classes. Generally, the        searching variable S(c,b,n) represents the maximum        log-likelihood for the segment sequence up to the frame b−1 plus        the log-likelihood of the segment from frame b to frame n being        in class c.    -   4. Score frames for n=1, . . . , T, i.e. all feature frames:    -   S(c,b,n)=S(c,b,n−1)+log(f_(c)(X_(n)), ∀b,c,n−b<2*D_(c), i.e.,        the current score at frame n could be derived from the previous        score at frame n−1. The searching variable for segments less        than twice the minimum duration is retained.    -   5. Retain a record of the optimal result at frame n (similarly,        segments under twice the minimum duration will be considered):

${S^{*}(n)} = {\max\limits_{{{c,b,{{2^{*}{Dc}} > {{9\; n} - b}}})} > {Dc}}\mspace{14mu}{S\left( {c,b,n} \right)}}$${B^{*}(n)} = {\underset{b,{({c,b,{{2^{*}{Dc}} > {({n - b})} > {Dc}}}}}{\arg\;\max}\mspace{14mu}{S\left( {c,b,n} \right)}}$${C^{*}(n)} = {\underset{c,{({c,b,{{2^{*}{Dc}} > {({n - b})} > {Dc}}}}}{\arg\;\max}\mspace{14mu}{S\left( {c,b,n} \right)}}$

-   -   6. Initialize new searching variables for segments starting at        frame n:    -   S(c,n,n)=S*(n), ∀ c    -   7. Iterate step 4 to step 6 until the last frame T.    -   8. Trace back to get the maximum likelihood segment sequence.

The very last segment of the maximum likelihood segment sequence is(C*(T),B*(T),T), i.e., the segment starting from frame B*(T) and endingwith frame T with class id of C*(T). We can obtain the rest segments inthe best sequence by using the following back-tracing procedure:

-   -   8.1. Initialize back-tracing:    -   t=T,m=1    -   S(m)=C*(t), B*(t), t)    -   8.2. Iterate back-tracing until t=0    -   C_current=C*(t)    -   t=B*(t)    -   If C*(t)=C_current, then do nothing; otherwise,    -   m=m+1, S(m)=(C*(t),B*(t),t)

Additional processing may be performed to further refine identificationof segments associated with the key child or an adult as sources. Asstated above, the language environment can include a variety of sourcesthat may be identified initially as the key child or an adult when thesource is actually a different person or device. For example, soundsfrom a child other than the key child may be initially identified assounds from the key child. Sounds from an electronic device may beconfused with live speech from an adult. Furthermore, some adult soundsmay be detected that are directed to another person other than the keychild. Certain embodiments of the present invention may implementmethods for further processing and refining the segmentation and segmentID to decrease or eliminate inaccurate source identifications and toidentify adult speech directed to the key child.

Further processing may occur concurrently with, or subsequent to, theinitial MD-GMM model described above. FIG. 5 illustrates one embodimentof an adaptation method for further processing the recording bymodifying models associated with the MD-GMM subsequent to an initialMD-GMM. In block 402, the audio engine 208 processes the recording usinga first MD-GMM. For example, the recording is processed in accordancewith the MD-GMM described above to perform an initial segmentation andsegment ID.

In block 404, the audio engine 208 modifies at least one model of theMD-GMM. The audio engine 208 may automatically select one or more modelsof the MD-GMM to modify based on pre-set steps. In some embodiments, ifthe audio engine 208 detects certain types of segments that may requirefurther scrutiny, it selects the model of the MD-GMM that is mostrelated to the types of segments detected to modify (or formodification). Any model associated with the MD-GMM may be modified.Examples of models that may be modified include the key child model withan age-dependent key child model, an electronic device model, aloudness/clearness model that may further modify the key child modeland/or the adult model, and a parentese model that may further modifythe key child model and/or the adult model.

In block 406, the audio engine 208 processes the recordings again usingthe modified models of the MD-GMM. The second process may result in adifferent segmentation and/or segment ID based on the modified models,providing a more accurate identification of the source associated witheach segment.

In block 408, the audio engine 208 determines if additional modelmodification is needed. In some embodiments, the audio engine 208analyzes the new segmentation and/or segment ID to determine if anysegments or groups of segments require additional scrutiny. In someembodiments, the audio engine 208 accesses data associated with thelanguage environment in data storage 210 and uses it to determine ifadditional model modification is necessary, such as a modification ofthe key child model based on the current age of the child. If additionalmodel modification is needed, the process returns to block 404 foradditional MD-GMM model modification. If no additional modelmodification is needed, the process proceeds to block 410 to analyzesegment sound. The following describes certain embodiments of modifyingexemplary models in accordance with various embodiments of the presentinvention. Other models than those described below may be modified incertain embodiments of the present invention.

Age-Dependent Key Child Model

In some embodiments of the present invention, the audio engine 208 mayimplement an age-dependent key child model concurrently with, orsubsequent to, the initial MD-GMM to modify the key child model of theMD-GMM to more accurately identify segments in which other children arethe source from segments in which the key child is the source. Forexample, the MD-GMM may be modified to implement an age-dependent keychild model during the initial or a subsequent segmentation and segmentID.

The key child model can be age dependent since the audio characteristicsof the vocalizations, including utterances and other sounds, of a keychild change dramatically over the time that the recorder 108 may beused. Although the use of two separate models within the MD-GMM, one forthe key child and one for other children, may identify the speech of thekey child, the use of an age-dependent key child model further helps toreduce the confusion between speech of the key child and speech of theother children. In one embodiment, the age-dependent key child modelsare: 1) less than one-year old, 2) one-year old, 3) two-years old, and4) three-years old. Alternative embodiments may use other age groupingsand/or may use groupings of different age groups. For example, otherembodiments could use monthly age groups or a combination of monthly andyearly age groups. Each of the models includes characteristicsassociated with sounds commonly identified with children of the agegroup.

In one embodiment of the present invention, the age of the key child isprovided to device 200 via input device 212 during a set-up orconfiguration. The audio engine 208 receives the age of the key childand selects one or more of the key child models based on the age of thekey child. For example, if the key child is one year and ten months old,the audio engine 208 may select key child model 2 (one-year-old model)and key child model 3 (two-years-old model) or only key child model 2based on the age of the key child. The audio engine 208 may implementthe selected key child model or models by modifying the MD-GMM models toperform the initial or a subsequent segmentation and segment ID.

Electronic Device Model

In order to more accurately determine the number of adult words that aredirected to the key child, any segments including sounds, such as wordsor speech, generated electronically by an electronic device can beidentified as such, as opposed to an inaccurate identification as livespeech produced by an adult. Electronic devices can include atelevision, radio, telephone, audio system, toy, or any electronicdevice that produces recordings or simulated human speech. In someembodiments of the present invention, the audio engine 208 may modify anelectronic device model in the MD-GMM to more accurately identifysegments from an electronic device source and separate them fromsegments from a live adult without the need to determine the content ofthe segments and without the need to limit the environment of thespeaker (e.g., requiring the removal of or inactivation of theelectronic devices from the language environment).

The audio engine 208 may be adapted to modify and use the modifiedelectronic device model concurrently with, or subsequent to, the initialMD-GMM process. In some embodiments, the electronic device model can beimplemented after a first MD-GMM process is performed and used to adaptthe MD-GMM for additional determinations using the MD-GMM for the samerecording. The audio engine 208 can examine segments segmented using afirst MD-GMM to further identify reliable electronic segments. Reliableelectronic segments may be segments that are more likely associated witha source that is an electronic device and include certain criteria. Forexample, the audio engine 208 can determine if one or more segmentsinclude criteria commonly associated with sounds from electronicdevices. In some embodiments, the criteria includes (1) a segment thatis longer than a predetermined period or is louder than a predeterminedthreshold; or (2) a series of segments having a pre-set source pattern.An example of one predetermined period is five seconds. An example ofone pre-set source pattern can include the following:

Segment 1—Electronic device source;

Segment 2—A source other than the electronic device source (e.g.,adult);

Segment 3—Electronic device source;

Segment 4—A source other than the electronic device source; and

Segment 5—Electronic device source.

The reliable electronic device segments can be used to adapt the MD-GMMto include an adaptive electronic device model for further processing.For example, the audio engine 208 may use a regular K-means algorithm asan initial model and tune it with an expectation-maximization (EM)algorithm. The number of Gaussians in the adaptive electronic devicemodel may be proportional to the amount of feedback electronic devicedata and not exceed an upper limit. In one embodiment, the upper limitis 128.

The audio engine 208 may perform the MD-GMM again by applying theadaptive electronic device model to each frame of the sequence todetermine a new adaptive electronic device log-likelihood score forframes associated with a source that is an electronic device. The newscore may be compared with previously stored log-likelihood scores forthose frames. The audio engine 208 may select the larger log-likelihoodscore based on the comparison. The larger log-likelihood score may beused to determine the segment ID for those frames.

In some embodiments, the MD-GMM modification using the adaptiveelectronic device model may be applied using a pre-set number ofconsecutive equal length adaptation windows moving over all frames. Therecording signal may be divided into overlapping frames having a pre-setlength. An example of frame length according to one embodiment of thepresent invention is 25.6 milliseconds with a 10 millisecond shiftresulting in 15.6 milliseconds of frame overlap. The adaptive electronicdevice model may use local data obtained using the pre-set number ofadaptation windows. An adaptation window size of 30 minutes may be usedin some embodiments of the present invention. An example of one pre-setnumber of consecutive equal length adaptation windows is three. In someembodiments, adaptation window movement does not overlap. The frameswithin each adaptation window may be analyzed to extract a vector offeatures for later use in statistical analysis, modeling, andclassification algorithms. The adaptive electronic device model may berepeated to further modify the MD-GMM process. For example, the processmay be repeated three times.

Loudness/Clearness Detection Model

In order to select the frames that are most useful for identifying thespeaker, some embodiments of the present invention use frame levelnear/far detection or loudness/clearness detection model.Loudness/clearness detection models can be performed using a LikelihoodRatio Test (LRT) after an initial MD-GMM process is performed. At theframe level, the LRT is used to identify and discard frames that couldconfuse the identification process. For each frame, the likelihood foreach model is calculated. The difference between the most probable modellikelihood and the likelihood for silence is calculated and thedifference is compared to a predetermined threshold. Based on thecomparison, the frame is either dropped or used for segment ID. Forexample, if the difference meets or exceeds the predetermined threshold,then the frame is used; but if the difference is less than thepredetermined threshold, then the frame is dropped. In some embodiments,frames are weighted according to the LRT.

The audio engine 208 can use the LRT to identify segments directed tothe key child. For example, the audio engine 208 can determine whetheradult speech is directed to the key child or to someone else bydetermining the loudness/clearness of the adult speech or soundsassociated with the segments. Once segmentation and segment ID areperformed, segment-level near/far detection is performed using the LRTin a manner similar to that used at the frame level. For each segment,the likelihood for each model is calculated. The difference between themost probable model likelihood and the likelihood for silence iscalculated and the difference is compared to a predetermined threshold.Based on the comparison, the segment is either dropped or processedfurther.

Parentese Model

Sometimes adults use baby talk or “parentese” when directing speech tochildren. The segments including parentese may be inaccuratelyassociated with a child or the key child as the source because certaincharacteristics of the speech may be similar to that of the key child orother children. The audio engine 208 may modify the key child modeland/or adult model to identify segments including parentese andassociate the segments with an adult source. For example, the models maybe modified to allow the audio engine 208 to examine the complexity ofthe speech included in the segments to identify parentese. Since thecomplexity of adult speech is typically much higher than child speech,the source for segments including relatively complex speech may beidentified as an adult. Speech may be complex if the formant structuresare well formed, the articulation levels are good, and the vocalizationsare of sufficient duration—consistent with speech commonly provided byadults. Speech from a child may include formant structures that are lessclear and developed and vocalizations that are typically of a lesserduration. In addition, the audio engine 208 can analyze formantfrequencies to identify segments including parentese. When an adult usesparentese, the formant frequencies of the segment typically do notchange. Sources for segments including such identified parentese can bedetermined to be an adult.

The MD-GMM models may be further modified and the recording furtherprocessed for a pre-set number of iterations or until the audio engine208 determines that the segment IDs have been determined with anacceptable level of confidence. Upon completion of the segmentation andsegment ID, the identified segment can be further analyzed to extractcharacteristics associated with the language environment of the keychild.

Child Vocalization, Cry, Vegetative-Sound/Fixed-Signal Detection(Classification)

During or after performing segmentation and segment ID, the audio engine208 may classify key child audio segments into one or more categories.The audio engine 208 analyzes each segment for which the key child isidentified as the source and determines a category based on the sound ineach segment. The categories can include vocalizations, cries,vegetative-sound, and fixed-signal sounds. Vocalizations can includewords, phrases, marginal syllables, including rudimentaryconsonant-vowel sequences, utterances, phonemes, sequence phonemes,phoneme-like sounds, protophones, lip-trilling sounds commonly calledraspberries, canonical syllables, repetitive babbles, pitch variations,or any meaningful sounds which contribute to the language development ofthe child, indicate at least an attempt by the child to communicateverbally, or explore the capability to create sounds. Vegetative-soundincludes non-vocal sounds related to respiration and digestion, such ascoughing, sneezing, and burping. Fixed-signal sounds are related tovoluntary reactions to the environment and include laughing, moaning,sighing, and lip smacking.

Cries are a type of fixed-signal sound, but are detected separatelysince cries can be a means of communication.

The audio engine 208 may classify key child audio segments usingrule-based analysis and/or statistical processing. Rule-based analysiscan include analyzing each key child segment using one or more rules.For some rules, the audio engine 208 may analyze energy levels or energylevel transitions of segments. An example of a rule based on a pre-setduration is segments including a burst of energy at or above the pre-setduration are identified as a cry or scream and not a vocalization, butsegments including bursts of energy less than the pre-set duration areclassified as a vocalization. An example of one preset duration is threeseconds based on characteristics commonly associated with vocalizationsand cries. FIG. 6 illustrates energy levels of sound in a segmentassociated with the key child and showing a series of consonant (/b/)and vowel (/a/) sequences. Using a pre-set duration of three seconds,the bursts of energy indicate a vocalization since they are less thanthree seconds.

A second rule may be classifying segments as vocalizations that includeformant transitions from consonant to vowel or vice versa. FIG. 6illustrates formant transitions from consonant /b/ to vowel /a/ and thenback to consonant /b/, indicative of canonical syllables and, thus,vocalizations. Segments that do not include such transitions may befurther processed to determine a classification.

A third rule may be classifying segments as vocalizations if the formantbandwidth is narrower than a pre-set bandwidth. In some embodiments, thepre-set bandwidth is 1000 Hz based on common bandwidths associated withvocalizations.

A fourth rule may be classifying segments that include a burst of energyhaving a first spectral peak above a pre-set threshold as a cry. In someembodiments, the pre-set threshold is 1500 Hz based on characteristicscommon in cries.

A fifth rule may be determining a slope of a spectral tilt and comparingit to pre-set thresholds. Often, vocalizations include more energy inlower frequencies, such as 300 to 3000 Hz, than higher frequencies, suchas 6000 to 8000 Hz. A 30 dB drop is expected from the first part of thespectrum to the end of the spectrum, indicating a spectral tilt with anegative slope and a vocalization when compared to pre-set slopethresholds. Segments having a slope that is relatively flat may beclassified as a cry since the spectral tilt may not exist for cries.Segments having a positive slope may be classified as vegetative-sound.

A sixth rule may be comparing the entropy of the segment to entropythresholds. Segments including relatively low entropy levels may beclassified as vocalizations. Segments having high entropy levels may beclassified as cries or vegetative-sound due to randomness of the energy.

A seventh rule may be comparing segment pitch to thresholds. Segmentshaving a pitch between 250 to 600 Hz may be classified as avocalization. Segments having a pitch of more than 600 Hz may beclassified as a cry or squeal, and a pitch of less than 250 Hz may beclassified as a growl.

An eighth rule may be determining pitch contours. Segments having arising pitch may be classified as a happy sound. Segments having afalling pitch may be classified as an angry sound.

A ninth rule may be determining the presence of consonants and vowels.Segments having a mix of consonants and vowels may be classified asvocalizations. Segments having all or mostly consonants may beclassified as a vegetative-sound or fixed-signal sound.

A rule according to various embodiments of the present invention may beimplemented separately or concurrently with other rules. For example, insome embodiments the audio engine 208 implements one rule only while inother embodiments the audio engine 208 implements two or more rules.Statistical processing may be performed in addition to or alternativelyto the rule-based analysis.

Statistical processing may include processing segments with a MD-GMMusing 2000 or more Gaussians in which models are created using Mel-scaleFrequency Cepstral Coefficients (MFCC) and Subband Spectral Centroids(SSC). MFCCs can be extracted using a number of filter banks withcoefficients. In one embodiment, 40 filter banks are used with 36coefficients. SSCs may be created using filter banks to capture formantpeaks. The number of filter banks used to capture formant peaks may be 7filter banks in the range of 300 to 7500 Hz. Other statisticalprocessing may include using statistics associated with one or more ofthe following segment characteristics:

Formants;

Formant bandwidth;

Pitch;

Voicing percentage;

Spectrum entropy;

Maximum spectral energy in dB;

Frequency of maximum spectral energy; and

Spectral tilt.

Statistics regarding the segment characteristics may be added to theMFCC-SSC combinations to provide additional classification improvement.

As children age, characteristics associated with each key child segmentcategory may change due to growth of the child's vocal tract. In someembodiments of the present invention, an age-dependent model may be usedin addition or alternatively to the techniques described above toclassify key child segments. For example, vocalization, cry, andfixed-signal/vegetative-sound models may be created for each age group.In one embodiment, 12 different models are used with Group 1corresponding to 1 to 2 months old, Group 2 corresponding to 3 to 4months old, Group 3 corresponding to 5 to 6 months old, Group 4corresponding to 7 to 8 months old, Group 5 corresponding to 9 to 10months old, Group 6 corresponding to 11 to 12 months old, Group 7corresponding to 13 to 14 months old, Group 8 corresponding to 15 to 18months old, Group 9 corresponding to 19 to 22 months old, Group 10corresponding to 23 to 26 months old, Group 11 corresponding to 27 to 30months old, and Group 12 corresponding to 31 to 48 months old. In analternative embodiment, vocalization, cry, andfixed-signal/vegetative-sound models may be created for each month ofage from 1 month to 48 months. This model will include 144 models, 48models for each category. Alternative embodiments may use a differentnumber of groups or associate different age ranges with the groups.

The audio engine 208 may also identify segments for which an adult isthe source. The segments associated with an adult source can includesounds indicative of conversational turns or can provide data formetrics indicating an estimate of the amount or number of words directedto the key child from the adult. In some embodiments, the audio engine208 also identifies the occurrence of adult source segments to key childsource segments to identify conversational turns.

In block 304, the audio engine 208 estimates key child segmentcharacteristics from at least some of the segments for which the keychild is the source, independent of content. For example, thecharacteristics may be determined without determining or analyzingcontent of the sound in the key child segments. Key child segmentcharacteristics can include any type of characteristic associated withone or more of the key child segment categories. Examples ofcharacteristics include duration of cries, number of squeals and growls,presence and number of canonical syllables, presence and number ofrepetitive babbles, presence and number of phonemes, protophones,phoneme-like sounds, word or vocalization count, or any identifiablevocalization or sound element.

The length of cry can be estimated by analyzing segments classified inthe cry category. The length of cry typically decreases as the childages or matures and can be an indicator of the relative progression ofthe child's development.

The number of squeals and growls can be estimated based on pitch,spectral intensity, and dysphonation by analyzing segments classified asvocalizations. A child's ability to produce squeals and growls canindicate the progression of the child's language ability as it indicatesthe key child's ability to control the pitch and intensity of sound.

The presence and number of canonical syllables, such as consonant andvowel sequences, can be estimated by analyzing segments in thevocalization category for relatively sharp formant transitions based onformant contours.

The presence and number of repetitive babbles may be estimated byanalyzing segments classified in the vocalization category and applyingrules related to formant transitions, durations, and voicing. Babblingmay include certain consonant/vowel combinations, including three voicedstops and two nasal stops. In some embodiments, the presence and numberof canonical babbling may also be determined Canonical babbling mayoccur when 15% of syllables produced are canonical, regardless ofrepetition. The presence, duration, and number of phoneme, protophones,or phoneme-like sounds may be determined. As the key child's languagedevelops, the frequency and duration of phonemes increases or decreasesor otherwise exhibits patterns associated with adult speech.

The number of words or other vocalizations made by the key child may beestimated by analyzing segments classified in the vocalization category.In some embodiments, the number of vowels and number of consonants areestimated using a phone decoder and combined with other segmentparameters such as energy level and MD-GMM log-likelihood differences. Aleast-square method may be applied to the combination to estimate thenumber of words spoken by the child. In one embodiment of the presentinvention, the audio engine 208 estimates the number of vowels andconsonants in each of the segments classified in the vocalizationcategory and compares it to characteristics associated with the nativelanguage of the key child to estimate the number of words spoken by thekey child. For example, an average number of consonants and vowels perword for the native language can be compared to the number of consonantsand vowels to estimate the number of words. Othermetrics/characteristics can also be used, including phoneme,protophones, and phoneme-like sounds.

In block 306, the audio engine 208 estimates characteristics associatedwith identified segments for which an adult is the source, independentof content. Examples of characteristics include a number of words spokenby the adult, duration of adult speech, and a number of parentese. Thenumber of words spoken by the adult can be estimated using similarmethods as described above with respect to the number of words spoken bythe key child. One example of a method to detect adult word count isbased on human annotated word-count, using Least-Squared LinearRegression to train. The model may also be guided or trained by humanannotated word-count. The duration of adult speech can be estimated byanalyzing the amount of energy in the adult source segments.

Language Environment Metric

In block 308, the audio engine 208 can determine one or more metricsassociated with the language environment using the key child segmentcharacteristics and/or the adult segment characteristics. For example,the audio engine 208 can determine a number of conversational turns or“turn-taking” by analyzing the characteristics and time periodsassociated with each segment. In some embodiments, the audio engine 208can be configured to automatically determine the one or more metrics. Inother embodiments, the audio engine 208 receives a command from inputdevice 212 to determine a certain metric.

Metrics can include any quantifiable measurement of the key child'slanguage environment based on the characteristics. The metrics may alsobe comparisons of the characteristics to statistical averages of thesame type of characteristics for other persons having similarattributes, such as age, to the key child. Examples of metrics includeaverage vocalizations per day expressed by the key child, averagevocalizations for all days measured, the number of vocalizations permonth, the number of vocalizations per hour of the day, the number ofwords directed to the child from an adult during a selected time period,and the number of conversational turns.

In some embodiments, metrics may relate to the key child's developmentalage. In the alternative or in addition to identifying delays andidiosyncrasies in the child's development as compared to an expectedlevel, metrics may be developed that may estimate causes of suchidiosyncratic and developmental delays. Examples of causes includedevelopmental medical conditions such as autism or hearing problems.

In block 310, the audio engine 208 outputs at least one metric to outputdevice 114. For example, the audio engine 208 may, in response to acommand received from input device 212, output a metric associated witha number of words spoken by the child per day to the output device 214,where it is displayed to the user. FIGS. 7-12 are screen shots showingexamples of metrics displayed on output device 214. FIG. 7 illustrates agraphical vocalization report showing the number of vocalizations perday attributable to the key child. FIG. 8 illustrates a graphicalvocalization timeline showing the number of vocalizations in a day perhour. FIG. 9 illustrates a graphical adult words report showing a numberof adult words directed to the key child during selected months. FIG. 10illustrates a graphical words timeline showing the number of words perhour in a day attributable to the key child. FIG. 11 illustrates agraphical representation of a turn-takings report showing the number ofconversational turns experienced by the key child on selected days permonth. FIG. 12 illustrates a graphical representation of a key child'slanguage progression over a selected amount of time and for particularcharacteristics.

Snapshot

In one embodiment, a series of questions are presented to the user toelicit information about the key child's language skills. The questionsare based on well-known milestones that children achieve as they learnto speak. Examples of questions include whether the child currentlyexpresses certain vocalizations such as babbling, words, phrases, andsentences. Once the user responds in a predetermined manner to thequestions, no new questions are presented and the user is presented witha developmental snapshot of the speaker based on the responses to thequestions. In one embodiment, once three “No” answers are entered,indicating that the child does not exhibit certain skills, the systemstops and determines the developmental snapshot. The questioning may berepeated periodically and the snapshot developed based on the answersand, in some embodiments, data from recording processing. An example ofa snapshot may include the language development chart shown in FIG. 12.In an alternative embodiment, the series of questions is answeredautomatically by analyzing the recorded speech and using the informationobtained to automatically answer the questions.

Certain embodiments of the present invention do not require that the keychild or other speakers train the system, as is required by many voicerecognition systems. Recording systems according to some embodiments ofthe present invention may be initially benchmarked by comparing certaindeterminations made by the system with determinations made by reviewinga transcript. To benchmark the performance of the segmenter, theidentification of 1) key child v. non-key child and 2) adult v.non-adult were compared, as well as the accuracy of the identificationof the speaker/source associated with the segments.

Although the foregoing describes the processing of the recorded speechto obtain metrics, such as word counts and conversational turns, othertypes of processing are also possible, including the use of certainaspects of the invention in conventional speech recognition systems. Therecorded speech file could be processed to identify a particular word orsequence of words or the speech could be saved or shared. For example, achild's first utterance of “mama” or “dada” could be saved much as aphoto of the child is saved or shared via e-mail with a family member.

Expressive Language Assessment

Each language has a unique set of sounds that are meaningfullycontrastive, referred to as a phonemic inventory. English has 42phonemes, 24 consonant phonemes and 18 vowel phonemes. A phoneme is thesmallest phonetic unit in a language that is capable of conveying adistinction in meaning. A sound is considered to be a phoneme if itspresence in a minimal word pair is associated with a difference inmeaning. For example, we know that /t/ and /p/ are phonemes of Englishbecause their presence in the same environment results in a meaningchange (e.g., “cat” and “cap” have different meanings). Followinglinguistic conventions, phonemes are represented between slashes, suchas /r/.

One embodiment that automatically assesses the key child's languagedevelopment uses a phone decoder from an automatic speech recognition(“ASR”) system used to recognize content from adult speech. One exampleis the phone detector component from the Sphinx ASR system provided byCarnegie Mellon University. The phone decoder recognizes a set of phonesor speech sounds, including consonant-like phones, such as “t” and “r”and vowel-like phones such as “er” and “ey”. ASR phones are approximatesof phonemes; they are acoustically similar to true phonemes, but theymay not always sound like what a native speaker would categorize asphonemic. These pseudo-phonemes are referred to herein as “phones” or“phone categories” and are represented using quotation marks. Forexample, “r” represents phone or phoneme-like sounds.

Models from systems designed to recognize adult speech have not beensuccessfully used to process child vocalizations due to the significantdifferences between adult speech and child vocalizations. Childvocalizations are more variable than adult speech, both in terms ofpronunciation of words and the language model. Children move from highlyunstructured speech patterns at very young ages to more structuredpatterns at older ages, which ultimately become similar to adult speechespecially around 14 years of age. Thus, ASR systems designed torecognize adult speech have not worked when applied to the vocalizationsor speech of children under the age of about 6 years. Even those ASRsystems designed for child speech have not worked well. The exceptionshave been limited to systems that prompt a child to pronounce aparticular predetermined word.

The variability of child speech also makes it difficult to developmodels for ASR systems to handle child vocalizations. Most ASR systemsidentify phonemes and words. Very young children (less than 12 months ofage) do not produce true phonemes. They produce protophones, which mayacoustically look and sound like a phoneme but are not regular enough tobe a phoneme and may not convey meaning. The phone frequencydistribution for a child is very different from the phone frequencydistribution for an adult.

For example, a very young child cannot produce the phoneme /r/, so notmany “r” phones appear. However, over time more and more “r” phonesappear (at least for an English-speaking child) until the child reallydoes produce the /r/ phoneme. A very young child may not attributemeaning to a protophone or phone. A child begins to produce truephonemes about the time that they start to talk (usually around 12months of age), but even then the phonemes may only be recognized bythose who know the child well. However, even before a child can producea true phoneme, the child's vocalizations can be used to assess thechild's language development.

Although an adult ASR model does not work well with child speech, oneembodiment of the present invention uses a phone decoder of an ASRsystem designed for adult speech, since the objective is to assess thelanguage development of a child independent of the content of thechild's speech. Even though a child does not produce a true phoneme, thephone decoder is forced to pick the phone category that best matcheseach phone produced by the child. By selecting the appropriate phonecategories for consideration, the adult ASR phone decoder can be used toassess child vocalizations or speech.

As shown with the “r” phone, there is some correlation between thefrequency of a phone and chronological age. The correlation can bepositive or negative. The relationship varies for different age rangesand is non-linear for some phones. FIG. 13 describes the correlationbetween selected phones and chronological age. As shown in FIG. 13,there is a positive correlation between age and the “r” phone and anegative correlation between age and the “b” phone. As shown in FIG. 14,the correlation can be non-linear over the age range of interest. Forexample, the correlation for the “1” phone is positive for ages 0 to 6months, 7 to 13 months, and 14 to 20 months, but then becomes negativefor ages 21 to 30 months and 31+ months.

To assess the language development of a child, one embodiment uses oneor more recordings taken in the child's language environment. Eachrecording is processed to identify segments within the recording thatcorrespond to the child with a high degree of confidence. Typically, therecording will be around 12 hours in duration in which the childproduces a minimum of 3000 phones. As described in more detail above,multiple models can be used to identify the key child segments,including, but not limited to, an age-based key child model, another-child model, a male adult model, a female adult model, anelectronic device model, a silence model, and a loudness/clearnessmodel. The use of these models allows the recording to be taken in thechild's language environment rather than requiring that the recording betaken in a controlled or clinical environment.

The phone decoder processes the high confidence key child segments(i.e., key child segments that are deemed to be sufficiently clear), anda frequency count is produced for each phone category. The frequencycount for a particular phone represents the number of times that theparticular phone was detected in the high confidence key child segments.A phone parameter PCn for a particular phone category n represents thefrequency count for that phone category divided by the total number ofphones in all phone categories. One particular embodiment uses 46 phonecategories where 39 of the phone categories correspond to a speech sound(see FIG. 13) and 7 of the phone categories correspond to non-speechsounds or noise (filler categories), such as sounds that correspond to abreath, a cough, a laugh, a smack, “uh”, “uhum,” “um” or silence. Otherembodiments may use phone decoders other than the Sphinx decoder. Sincedifferent phone decoders may identify different phone categories and/ordifferent non-phone categories, the particular phone and non-phonecategories used may vary from that shown in FIGS. 12 and 13. Tocalculate an expressive language index z-score for the key child,ELZ(key child), the phone parameters PCn are used in the followingequation:ELZ(key child)=b1(AGE)*PC1+b2(AGE)*PC2+ . . . +b46(AGE)*PC46  (1)The expressive language index includes a weight bn(age) associated witheach phone category n at the age (AGE) of the key child. For example,b1(12) corresponds to the weight associated with phone category 1 at anage of 12 months, and b2(18) corresponds to the weight associated withphone category 2 at an age of 18 months. The weights bn(age) in theexpressive language index equation may differ for different ages, sothere is a different equation for each monthly age from 2 months to 48months. In one embodiment, the equation for a 12-month-old child usesthe weights shown in the “12 months” column in FIG. 15. The derivationof the values for the weights bn(age) is discussed below.

To enhance interpretability and to conform to the format that iscommonly used in language assessments administered by speech languagepathologists (“SLPs”), such as PLS-4 (Preschool Language Scale—4) andREEL-3 (Receptive Expressive Emergent Language—3), the expressivelanguage index can be standardized. This step is optional. Equation (2)modifies the distribution from mean=0 and standard deviation=1 tomean=100 and standard deviation=15 to standardize the expressivelanguage index and to produce the expressive language standard scoreELSS.ELSS=100+15*ELZ(Key Child)  (2)SLP-administered language assessment tools typically estimatedevelopmental age from counts of observed behaviors. Using a largesample of children in the age range of interest, developmental age isdefined as the median age for which a given raw count is attained. Inone embodiment of the system, the phone probability distribution doesnot generate raw counts of observed behaviors, and development age isgenerated in an alternative approach as an adjustment upward or downwardto a child's chronological age. In this embodiment, the magnitude of theadjustment is proportional both to the expressive language standardscore (ELSS) and to the variability in ELSS observed for the child'schronological age.

Boundary conditions are applied to prevent nonsensical developmental ageestimates. The boundary conditions set any estimates that are greaterthan 2.33 standard deviations from the mean (approximately equal to the1st and 99th percentiles) to either the 1st or 99th percentiles. Anage-based smoothed estimate of variability is shown below in equation(3). The determination of the values shown in equation (3) other thanage is discussed below.SDAGE=0.25+0.02*Age  (3)

To determine the child's expressive language developmental age, ELDA,the child's chronological age is adjusted as shown below in equation(4). The determination of the constant value shown in equation (4) isdiscussed below.ELDA=Chronological Age+Constant*SDAGE*ELSS  (4)In one embodiment for a 12 month old, the expressive languagedevelopmental age is calculated using a chronological age of 12 and aconstant of 7.81 as shown below:ELDA=12+7.81*SDAGE*ELSS  (5)

The system can output the child's EL standard score, ELSS, and thechild's EL developmental age, ELDA. Alternatively, the system cancompare the child's chronological age to the calculated developmentalage and based on the comparison output a flag or other indicator whenthe difference between the two exceeds a threshold. For example, if theELSS is more than 1.5 standard deviations lower than normal, then amessage might be output suggesting that language development may bedelayed or indicating that further assessment is needed.

The validity of the EL model was tested by comparing EL standard scoresand EL developmental ages to results derived from the assessmentsadministered by the SLPs. The EL developmental age correlated well withchronological age (r=0.95) and with the age estimate from the SLPadministered assessments at r=0.92. The EL standard score is an accuratepredictor of potential expressive language delay. Using a thresholdscore of 77.5 (1.5 standard deviations below the mean), the EL standardscore correctly identified 68% of the children in one study who fellbelow that threshold based on an SLP assessment. Thirty-two percent ofthe children identified as having possible delays had below average ELscores, but did not meet the 77.5 threshold score. Only 2% of thenon-delayed children were identified as having possible delay based ontheir EL score.

One way of increasing the accuracy of the EL assessment is to averagethe EL scores derived from three or more recording sessions. Oneembodiment averages three EL scores derived from three recordings madeon different days for the same key child. Since the models are based onan age in months, the recordings should be taken fairly close togetherin time. Averaging three or more EL scores increases the correlationbetween the EL scores and the SLP assessment scores from r=0.74 tor=0.82.

Combining the EL developmental age with results from a parentquestionnaire also increases the accuracy of the EL assessment. The LENADevelopmental Snapshot questionnaire is one example of a questionnairethat uses a series of questions to the parent to elicit informationabout important milestones in a child's language development, such asidentifying when the child begins to babble, uses certain words, orconstructs sentences. The LENA Developmental Snapshot calculates adevelopmental age based on the answers to the questions. Thequestionnaire should be completed at or very near the time the recordingsession takes place. By averaging the developmental age calculated bythe questionnaire and the developmental age calculated by the ELassessment, the correlation between the calculated estimate and the SLPestimate increases to approximately r=0.82. If three or more EL scoresand the questionnaire results are averaged, then the correlation is evengreater, approximately r=0.85. Methods other than simple averaginglikely will yield even higher correlations. If the questionnaireincludes questions directed to receptive language development, as wellas expressive language development, then the correlation may be evengreater.

Although the foregoing example detects single phones and uses thefrequency distribution of the single phones to estimate a standard scoreand developmental age, it may also be possible to use the frequencydistribution for certain phone sequences in a similar manner. Forexample, it may be possible to use the frequency distributions of bothsingle phones and phone sequences in an equation that includes differentweights for different single phones and phone sequences for differentages. In one embodiment, bi-phone sequences may be used instead ofsingle phones and in another embodiment, tri-phone sequences may beused. In yet another embodiment, combinations of phones and bi-phones orphones, bi-phones, and tri-phones may be used. The invention is notlimited in use to phones, bi-phones, or tri-phones.

Bi-phone (or the usage of more than one phone) allows for theincorporation of sequence information. In language, phones tend to occurin a logical sequence; therefore, additional resolution is gained byanalyzing not just the phones but the sequence of the phones. Bi-phonesare defined as each pair of adjacent phones in a decoded sequence. Forexample, the decoded phone sequence “P A T” contains the phone pairs“P-A” and “A-T”. Following from the above example, a tri-phone sequencein this case would be “P A T.” Note that uni-phones are included as asingle phone paired with an utterance start or stop marker.

The bi-phone frequencies then are used as the input to the same type oflinear regression models described above for the uni-phone case. Theintroduction of bi-phone or tri-phone also introduces a challengingtechnical issue, i.e., the dimension of bi-phone (total number ofbi-phone) is significantly larger than uni-phone (n-squared versus n),and the dimension of tri-phone (n-raised-power-to-3) is even much biggerthan that of both bi-phone and uni-phone. Given 46 phone categories plusthe utterance start and end markers, the total number of possible pairsis 48*48=2304. It may be problematic to include such high dimensionalinput to a linear regression; the sheer number of predictors couldeasily lead to the trained regression model overfitting to the trainingdata, resulting in poor generalization to novel samples. It is possiblethat, with a sufficient amount of data, this issue will cease to exist.The large dimension makes the model size bigger which needs much moredata to train. Principal Component Analysis (PCA) is used to reduce thelarge dimension to small ones. For bi-phone, the current data shows thatthe dimension reduced from 2000 to around 50 gives the best result.

To resolve this issue, in one alternative embodiment, principlecomponent analysis (PCA) is used to reduce the dimensions of thebi-phone space from over 2300 to under 100. PCA is a data-drivenstatistical analysis tool for data compression, dimension reduction,etc. The much lower dimensioned subspace of the data with the most data“spread” or “distribution” is the principal component subspace to besearched. For a one-dimension subspace, the data “spread” could bequantified as the variance. Extensive experimentation has suggested thatreducing the bi-phone PCA space to 50 dimensions provided optimalresults. The over 2300 bi-phone combinations were reduced to 50principal components to use as predictors in multiple linear regressionmodels predicting SLP-based scores, exactly as described above in theuni-phone case. The bi-phone approach to estimating improves thecorrelation with SLP-based expressive language composite scores (r=0.75,p<0.01) compared to the uni-phone approach (r=0.72, p<0.01), both underthe leave-one-child-out cross-validation method.

The following is a brief description of PCA. For a set of data{x_(i)|i=1, . . . , n}, the PCA optimal linear transform could beconstructed in the following way:

1. Calculate covariance matrix S=Σ(x_(i)−m)(x_(i)−m)^(T) where m is themean of the data set.

2. Calculate sorted eigenvalues and associated eigenvectors:

[λ₁, λ₂, . . . , λ_(n)], [v₁, . . . , v_(n)] where Sv_(i)=λ_(i)v_(i) andλ_(i)≧λ_(i+1).

3. To reduce the dimension after linear transform, the first mcomponents could be chosen to construct linear transform, where m<n.

4. The new feature would be y=[v₁, . . . , v_(m)]^(T) x.

In the actual experiments, the first step was tried with mean removedand without mean removed. For the current data, there is no fundamentaldifference between them.

Another alternative embodiment uses phone duration rather than phonefrequency. In this embodiment, the phone decoder determines the lengthof time or duration for each phone category. A phone duration parameterPCn for a particular phone category n represents the duration for thatphone category divided by the total duration of phones in all phonecategories. To calculate an expressive language index z-score for thekey child, the phone duration parameters are used in an equation that issimilar to equation (1), but that uses different weights. The weightsmay be calculated in a matter similar to that used to calculate weightsfor frequency distribution.

Estimated Mean Length of Utterance

Speech and language professionals have traditionally used “mean lengthof utterance” (MLU) as an indicator of child language complexity. Thismeasurement, originally formalized by Brown, assumes that since thelength of child utterances increases with age, one can derive areasonable estimate of a child's expressive language development byknowing the average length of the child's utterances or sentences. SeeBrown, R., A First Language: The Early Stages, Cambridge, Mass., HarvardUniversity Press (1973). Brown and others have associated utterancelength with developmental milestones (e.g., productive use ofinflectional morphology), reporting consistent stages of languagedevelopment associated with MLU. Utterance length is considered to be areliable indicator of child language complexity up to an MLU of 4 to 5morphemes.

To aid in the development of an MLU-equivalent measure based on phonefrequency distributions, transcribers computed the MLU for 55 children15 to 48 months of age (approximately two children for each age month).The transcribers followed transcription and morpheme-counting guidelinesdescribed in Miller and Chapman, which were in turn based on Brown'soriginal rules. See Miller, J. F. & Chapman, R. S., “The Relationbetween Age and Mean Length of Utterance in Morphemes”, Journal ofSpeech and Hearing Research, Vol. 24, pp. 154-161 (1981). Theyidentified 50 key child utterances in each file and counted the numberof morphemes in each utterance. The MLU was calculated by dividing thetotal number of morphemes in each transcribed file by 50.

In addition to the expressive language standard score (ELSS) anddevelopmental age (ELDA), the system produces an Estimated Mean Lengthof Utterance (EMLU). In one embodiment, the EMLU may be generated bypredicting human-derived MLU values directly from phone frequency orphone duration distributions, similar to the estimate of the expressivelanguage estimate ELZ. In another embodiment, the EMLU may be generatedbased on simple linear regression using developmental age estimates topredict human-derived MLU values. For example,EMLU=0.297+0.067*ELDA  (6).Derivation of Equation Values

To aid in the development of the various models used to analyze childspeech described herein, over 18,000 hours of recordings of 336 childrenfrom 2 to 48 months of age in their language environment were collected.Hundreds of hours of these recordings were transcribed, and SLPsadministered over 1900 standard assessments of the children, includingPLS-4 and/or REEL-3 assessments. The vast majority of the recordingscorrespond to children demonstrating normal language development. Thisdata was used to determine the values in equations (1), (2)-(5), and(6).

For example, the observations and assessments for each child wereaveraged together and transformed to a standard z-score to produce anexpressive language index value for each child for a particular age. Thephone category information output from the Sphinx phone decoder was usedalong with multiple linear regression to determine the appropriateweights for the expressive language index for each age.

An iterative process was used to determine the set of weights (b1(AGE)to b46(AGE)) for equation (1). In the first step, data for children of acertain month of age were grouped together to determine a set of weightsfor each age group. For example, data from 6-month olds was used tocreate a set of weights for the expressive language index for a 6-monthold. In the next step, data for children of similar ages was groupedtogether to determine a different set of weights for each age group. Forexample, data from 5-, 6-, and 7-month olds was used to create adifferent set of weights for the expressive language index for a 6-monthold. In subsequent steps, data for children of additional age rangeswere included. For example, data from 4-, 5-, 6-, 7-, and 8-month oldswere used to create a different set of weights for the expressivelanguage index for a 6-month old, etc. This process was repeated for allage months and across increasingly broad age ranges. A dynamicprogramming approach was used to select the optimal age range andweights for each monthly age group. For example, in one embodiment, atage 12 months, the age band is from age 6 months to age 18 months andthe weights are shown in the table in FIG. 15. FIG. 15 also illustratesthe weights for another example for a key child aged 6 months with anage band from 3 months to 9 months, and the weight for a key child aged18 months with an age band from 11 months to 25 months. Although the ageranges in these examples are symmetric, the age ranges do not have to besymmetric and typically are not symmetric for ages at the ends of theage range of interest.

The calculated weights were tested via the method of Leave-One-OutCross-Validation (LOOCV). The above iterative process was conducted oncefor each child (N=336), and in each iteration the target child wasdropped from the training dataset. The resultant model was then used topredict scores for the target child. Thus, data from each participantwas used to produce the model parameters in N−1 rounds. To confirm themodel, the Mean Square Error of prediction averaged across all modelswas considered. The final age models included all children in theappropriate age ranges.

Exemplary EL System

FIG. 16 illustrates a block diagram for an exemplary system thatcomputes an EL score and developmental age as described above. Theillustrated system includes a digital recorder 1602 for recording audioassociated with the child's language environment. The recorded audio isprocessed by the feature extraction component 1604 and segmentation andsegment ID component 1606 to extract high confidence key child segments.A phone decoder 1608 based on a model used to recognize content fromadult speech processes the high confidence key child segments 1607. Thephone decoder provides information on the frequency distribution ofcertain phones to the EL component 1610. The EL component uses theinformation to calculate the EL score, estimate the developmental age,and/or estimate the mean length of utterances as described above. TheReports and Display component 1612 outputs the EL information asappropriate.

Although FIG. 16 illustrates that a recording is processed using asystem that processes recordings taken in the child's languageenvironment, such as the LENA system, the EL assessment can operate withkey child segments generated in any manner, including recordings takenin a clinical or research environment, or segments generated using acombination of automatic and manual processing.

Autism Detection

In one embodiment, a system and method for detecting autism uses theautomatic language processing system and methodologies described above.Recordings captured in a natural language environment are processed, anda model of the language development of those known subjects is created.By using a large enough sample, trends in language development can bedetermined. This is referred to as normative trends. Generally, if thereis a particular developmental disorder that is desired to be studied,then the language of individuals having the disorder and normalindividuals is studied and trends are developed. The methodologydescribed herein is an example of how a particular developmentaldisorder, autism, may be detected using language analysis. The methodand system, however, may be applied to a variety of disorders anddiseases, for example autism and Alzheimer's disease. All diseases anddisorders that may be detected through the analysis of language may bedetected through this embodiment.

In the case of autism, aberrations in the voice of individuals have beennoted in the descriptions of Autism Spectrum Disorders (ASD). It hasbeen shown in numerous studies that autism is indeed associated withabnormalities of vocal quality, prosody, and other features of speech.See R. Paul, A. Augustyn, A. Klin, F. R. Volkmar, Journal of Autism andDevelopmental Disorders 35, 205 (2005); W. Pronovost, M. P. Wakstein, D.J. Wakstein, Exceptional Children 33, 19 (1966); and S. J. Sheinkopf, P.Mundy, D. K. Oller, M. Steffens, Journal of Autism and DevelopmentalDisorders 30, 345 (2000). However, these features of speech are noteasily detected or identified; therefore, the definition of autism(DSM-IV-TR, APA, 2000) does not include a description of what suchfeatures may include.

In this embodiment, autism may be affirmatively detected based onpositive markers based on the characteristics of speech that could notpreviously be performed. Generally, autism is detected by using“negative markers”, such as a deficit in joint attention. See, forexample: S. Baron-Cohen, J. J Allen, C. Gillberg, The British Journal ofPsychiatry 161, 839 (1992); K. A. Loveland, S. H. Landry, Journal ofAutism and Developmental Disorders 16, 335 (1986); and P. Mundy, C.Kasari, M. Sigman, Infant Behavior and Development 15, 377 (1992).

The method used in determining autism in children may be described asChild Speech Analysis using Transparent Parameters (CSATP). Roughly,Transparent Parameters are those parameters that may be extracted fromthe sound signal and are independent of the actual content of the soundsignal in terms of meaning of the language or sounds produced.Transparent parameters are discussed further below. CSATP includes anumber of steps: segmentation; VOC, CRY, and VEGFIX Classification andvocalization count; acoustic analysis; extraction of transparentparameters; and data set classification. Using this methodology and asample of sufficient size of children having normal speech development,delayed speech development, and autism, trends in language may bedeveloped for these groups. See the above discussion of VOC, CRY, andVEGFIX classification in relation to audio engine 208 that may classifykey child audio segments into one or more categories.

FIGS. 17 and 18 show a flow chart for a method of detecting autism and amethod of creating trends for use in the method of detecting autism,respectively. The segmentation of block 1810 and 1835 is performed asdescribed above in reference to FIG. 4 and block 304. In block 1810, thesegmentation is performed on data for an individual key child, and inblock 1835 the segmentation is performed on a plurality of recordings ofnormal, delayed, and autistic children. During segmentation, the speakeris identified for a particular piece of a recording. After the speakerhas been identified, the language from the speaker of interest, in thiscase the key child, is analyzed further. FIG. 19 shows the segmentationprocess in the top graph and the further break down of key childsegments into VOC, CRY, and VEGFIX segments.

The segments identified as belonging to a key child in blocks 1810 and1835 are then broken down into vocalizations (VOC), cries (CRY), andvegetative-sound and fixed-signal sounds (VEGFIX) in blocks 1815 and1840 respectively. Vocalizations include various types of speechdepending on the age of the child. Between 0 to 4 months, vocalizationsinclude only vowel-like sounds. Around 5 months, a child startsvocalizing marginal syllables which consist of very rudimentaryconsonant-vowel sequences. Some children make lip-trilling sounds calledraspberries, which are also considered as vocalizations. Around sevenmonths, a child's vocalizations may include canonical syllables andrepetitive babbles which are well constructed consonant and vowelsequences. At this stage, a child may explore with variation of pitchcreating high pitched squeals and low pitched and dysphonated growls.Around a year, a child starts saying isolated words, but keeps babblingtoo until 18 months or so. By two years, a child will have a fairlylarge vocabulary of spoken words. In short, vocalizations include allmeaningful sounds which contribute to the language development of thechild.

Vegetative-sound includes all non-vocal sounds related to respirationand digestion, e.g., coughing, sneezing, and burping. Fixed-signals aresounds which are related to the voluntary reactions to the environment,e.g., laughing, moaning, sighing, and lip smacking. Vegetative-sound andfixed-signal sounds are detected collectively. These types of sounds areeliminated since they do not provide information about linguisticsophistication.

It should be noted that cries are also a type of fixed-signal. Unlikeother fixed-signals, cries are very frequent (depending on the age) andconvey various emotional feelings and physical needs. Although notperformed in this specific method, the analysis of cries according tothe described techniques may be used to detect disorders or diseases,since crying is also another means of communication in a baby's life.

Child speech classification is performed by statistical processing usingMel-scale Frequency Cepstral Coefficients (MFCC) and Subband SpectralCentroids (SSC). Other statistical processing techniques may be used.

Using MFCC is a standard state-of-the-art method for automatic speechrecognition. Another available type of feature, albeit less popular thanMFCC, is SSC. In conventional MFCC features, the power spectrum in agiven subband is smoothed out, so that only the weighted amplitude ofthe power spectrum is kept, while in SSC the centroid frequency of eachsubband is extracted. SSC's can track the peak frequency in each subbandfor the speech section, while for the non-speech section it stays at thecenter of the subband. MFCC is a better feature than SSC by itself, butthe combination of MFCC and SSC demonstrates better performance for theautomatic speech recognition of adult speech. SSC has been applied forvarious applications—some of them are listed below:

Adult speech recognition

Speaker authentication or recognition

Timbre recognition of percussive sounds

While MFCC is good for extracting the general spectral features, SSCwill be useful in detecting the formant peaks. Since formant tracks arefound in child vocalizations (although voiced cries may have formanttracks) and not in vegetative-sound/fixed-signal sounds, the formantcontours can be tracked in child speech processing.

For child speech processing, a Fixed Boundary Gaussian Mixture Model(FB-GMM) classifiers with 2000 Gaussians are used, i.e., statisticalclassification is performed for every energy island as identified in theprevious stage. The models are created using two sets of features: MFCCand SSC. MFCC's are extracted using 40 filter banks with 36coefficients. SSC's are created using 7 filter banks to capture theformant peaks only. Since the audio used in this study has a samplingfrequency of 16 KHz, filter banks in the range of 300 to 7500 Hz areused. Hence, MFCC-SSC features have dimensions of (36+7=) 43, and withdelta information it becomes (43*2=) 86.

In the context of age dependent modeling, the purpose is to classifythree types of speech—vocalizations, cries, andfixed-signal/vegetative-sound sounds. However, these three categories ofchild speech vary immensely with the variation of age. Hence, one modelfor the entire age range 0 to 48 months will not serve our purpose.Several studies show that a child's vocal tract may grow from around 5cm to 12 cm from birth to four years old. Other studies show thatformant frequencies are highly dependent on the length of the vocaltract. By the theory of “open tube model of vocal tract”, therelationship between F^(i), i-th formant frequency, and l, the vocaltract length, is given by

${F_{i} = {\frac{c}{4\; l}\left( {{2\; i} - 1} \right)}},$where c is the speed of sound in air (moist air inside the mouth, atbody temperature, and appropriate pressure). This shows that the largerthe vocal tract length, the smaller the formant frequencies. Hence, dueto rapid growth of the vocal tract in babies, formant frequencies changeand, consequently, the overall speech characteristics change almostevery month of age. Hence, three models—/voc/, /cry/, and /vegfix/ arecreated for each month-age of the child ranging from 0 to 48 months.

Classification is done with prior knowledge of the child's age, by usingage dependent vocalization, cry, and fixed-signal/vegetative-soundmodels.

In blocks 1820 and 1845, acoustic analysis is performed on the VOCislands (recordings corresponding to periods of very high energy boundedby periods of very low energy). The islands within the child segmentsthen are further analyzed using acoustic features. The followingacoustic features are extracted from the VOC islands:

1. Duration Analysis:

-   -   It is assumed that every burst of energy which composes the        child speech has to be of certain duration to be considered as a        meaningful speech (vocalization). For example, if a continuous        energy section is more than 3 seconds, it can be assumed that        the speech is not a vocalization, but is most likely to be some        sort of cry or scream (based on other criteria). FIG. 6 shows an        example of a vocalization, which is a series of consonant vowel        sequences, (hi-ba-ba-bab-bab). Only the vowels are the high        energy parts while the consonants have low energy. The duration        of the high energy parts are measured for validation of        vocalization.

2. Canonical Syllable Identification:

-   -   Formant transitions (mainly for F1 and F2) can be noticed in CV,        VC, CVC or VCV sequences. FIG. 6, which is a series of CV and        CVC sequences, shows formant transitions from /b/ to the        following vowel /a/ and then to /b/. These types of formant        movements are indicative of canonical syllables which are part        of vocalizations.

3. Articulation Analysis:

-   -   Formant bandwidths mark the clarity of pronunciation. The        narrower the bandwidth, the clearer the speech. It is expected        that cries or other fixed-signals (e.g., lip smacking) or        vegetative-sound will have wider bandwidths than a true        vocalization. FIG. 20 shows an empirical display for how the        grouping of F1 and F2 bandwidths can mark the articulation        level. A score is assigned to each articulation group based of        the “goodness” of each articulation level.

4. Emotional Intensity Analysis:

-   -   High intensity speech sounds (e.g., a cry with a full lung of        air) are observed to have the first spectral peak above 1500 Hz.        Normal vocalizations will have more energy in the lower        frequencies (ranging from 300 to 3000 Hz) than higher        frequencies (6000 to 8000 Hz). Thus, there will be a 30 dB drop        expected from the first part of the spectrum to the end of the        spectrum, which is referred to as spectral tilt with a negative        slope. For cries, the spectral tilt may not exist, where the        spectrum is rather flat. A spectral tilt with a positive slope        (low energy in lower frequencies and high energy in higher        frequencies) indicates non-vocal sound (e.g., breathing, lip        smacking).

5. Dysphonation Analysis:

-   -   It is assumed that normal vocalizations which are mostly        composed of vowels makes the spectrum periodic. On the other        hand, dysphonated sounds have rather random spectrums with        subharmonics in the spectrum. The randomness of the spectrum can        be measured by the entropy of the spectrum. The higher the        entropy, the more random is the spectrum and the higher the        dysphonation.

6. Pitch Analysis:

-   -   Pitch is used to detect squeals and growls. Normal pitch for a        child is in the range of 250 to 600 Hz. A vocalization is marked        as a squeal if the pitch is more than 600 Hz (it could go up to        3000 Hz). Similarly, growls are vocalizations which have pitch        lower than 250 Hz.

7. Intonation Analysis:

-   -   Intonation has a major role in determining the emotion of the        child. Squeals and growls are vocalizations only when they are        playful and happy. Angry versions of those high- or low pitched        and dysphonated sounds are cries. Pitch contours help determine        whether the speech is angry or happy. Typically, a rising pitch        is an indicator of happy sounds, while a falling pitch is a sad        sound.

8. Voicing Analysis:

-   -   It is assumed that vocalizations are mostly composed of vowels,        which are voiced speech, with interlaced consonants (unvoiced        speech). If an entire speech section is unvoiced, then it is        assumed to be some sort of a vegetative-sound/fixed-signal sound        (e.g., cough, throat clearing, etc.).

For this analysis, formant peaks and formant bandwidths are detectedusing Linear Predictive (LP) analysis, while pitch is calculated basedon autocorrelations. Finally, formant and pitch contours are extractedby applying a smoothing filter—median filter. Other spectrum analysesare performed using a 1024 point FFT.

In blocks 1825 and 1850 of FIGS. 17 and 18, the transparent parametersare extracted. These parameters are used to determine whether a subjectis normative or autistic. FIG. 21 shows acoustic parameters pertinent tothe determination of autism. FIGS. 21 and 22 show additional acousticand non-acoustic parameters that may be extracted from recordings. Inthe present embodiment, the acoustic parameters depicted in FIGS. 21 and22 are used for the detection of autism. Alternatively, the non-acousticparameters depicted in FIG. 22 may be used for the detection of autism.Collectively, these acoustic and non-acoustic parameters are referred toas transparent parameters. It has been shown through utilizing themethodology of the present embodiment that there are differences betweenthe transparent parameters observed in normal, delayed, and autisticchildren. Generally, the acoustic parameters relate to thosevocalizations created by the key child, and non-acoustic parameters arethose relating to the interactions, specifically those interactionsbetween the key child and adults, and the environment that the childexperiences.

The nine non-acoustic parameters are shown in FIG. 22. The adultvocalization length in seconds refers to the length of adultvocalization on the recording. The adult vocalization count refers tothe number of vocalizations made by an adult. The number ofchild-initiated conversations refers to the number of times a childmakes a vocalization and an adult replies. The number of conversationalturns refers to the number of times a child responds to an adultvocalization. The number of conversational turns in child-initiatedconversations refers to when a child initiates a conversation and thenresponds to an adult vocalization thereafter. The child vocalizationlength in seconds in conversational turns refers to the length of timechild vocalizations last in conversational turns. The child vocalizationcounts in conversational turns refer to the number of vocalizations achild makes in a conversational turn (which may indicate the complexityof an answer). The child vocalization length in conversations with anadult is the average vocalization length of a child over a conversationwith an adult. The child vocalization counts in conversations with andadult is the number of vocalizations made by a child over a delineatedconversation with an adult.

The twelve acoustic parameters shown in FIG. 21 are both theoretically(based on models from 30 years of research in vocal development) andstatistically (as indicated by principal components analysis, PCA)clustered into four groupings pertaining to the infrastructure forspeech. Each of the twelve parameters are classified as a plus or minus.To adjust for differences in rate of vocalization (volubility) acrossindividual children and recordings as well as differences in lengths ofrecordings, for each parameter the ratio of the number of vocalizationslabeled plus to the number of utterances is taken. This yields a set of12 numbers (one for each parameter) per recording. This 12-dimensionalvector is used to predict vocal development and to classify recordingsas belonging to typically developing or autistic children in theanalyses.

As shown in FIG. 23, a large data set having children with ages spanningage 2-48 months was used. There were 2682 recordings of 328 children inthe same set which showed normal development. There were 300 recordingsof 30 children which showed delay in language development. There were225 recordings of 34 children who were diagnosed as autistic. From thisdata set, the model and trend lines were created.

In block 1855 of FIG. 18, trends are created based on the recordingscollected to be used as a model. A predicted vocal development score isdeveloped based on analysis of transparent parameters as will beexplained below. FIGS. 24-29 show trend lines and data points forpredicted vocal development scores. FIG. 24 shows a trend chart foracoustic parameters in autistic and normally developing children. Thegray dots represent the vocal development scores for normally developingchildren. The gray line is a trend line for normally developingchildren. The asterisks represent vocal development scores for autisticchildren. The diamonds represent the average (based on multiplerecordings for a single child) vocal development scores for autisticchildren. The black trend line is for autistic children. FIG. 25 shows atrend chart for acoustic parameters in autistic, normally developing,and language delayed children. The gray stars represent the average(based on multiple recordings for a single child) vocal developmentscores for language delayed children. The black diamonds represent theaverage (based on multiple recordings for a single child) vocaldevelopment scores for autistic children. The gray trend line is forlanguage delayed children. The black trend line is for autisticchildren. The broken trend line is for normally developing children.FIG. 26 shows a trend chart for acoustic parameters in normallydeveloping and language delayed children. The gray dots represent thevocal development scores for normally developing children. The asterisksrepresent vocal development scores for language delayed children. Theblack stars represent the average (based on multiple recordings for asingle child) vocal development scores for language delayed children.The black trend line is for language delayed children. The gray trendline is for normally developing children.

FIG. 27 shows non-acoustic parameters in normally developing andautistic children. The gray dots represent the vocal development scoresfor normally developing children. The gray line is a trend line fornormally developing children. The asterisks represent vocal developmentscores for autistic children. The diamonds represent the average (basedon multiple recordings for a single child) vocal development scores forautistic children. The black trend line is for autistic children. FIG.28 shows a trend chart for acoustic parameters in autistic, normallydeveloping, and language delayed children. The gray stars represent theaverage (based on multiple recordings for a single child) vocaldevelopment scores for language delayed children. The black diamondsrepresent the average (based on multiple recordings for a single child)vocal development scores for autistic children. The gray trend line isfor language delayed children. The black trend line is for autisticchildren. The broken trend line is for normally developing children.FIG. 29 shows a trend chart for acoustic parameters in normallydeveloping and language delayed children. The gray dots represent thevocal development scores for normally developing children. The asterisksrepresent vocal development scores for language delayed children. Theblack stars represent the average (based on multiple recordings for asingle child) vocal development scores for language delayed children.The black trend line is for language delayed children. The gray trendline is for normally developing children. As shown in FIGS. 24-29, thepredicted vocal development score by employing acoustic or non-acousticparameters for the population studied can be projected versus the age inmonths of the child.

The creation of a predicted vocal development score is based on analysisof transparent parameters (including acoustic or non-acoustic). Forexample, in a case of acoustic parameters, multiple linear regression(MLR) analysis can be conducted to obtain perspective on bothdevelopment and group differentiation. In one experiment using acousticparameters (shown in FIG. 21), the 12 acoustic parameter ratios ofspeech-related vocal islands (SVIs, previously referred to as VOCislands) to speech-related child utterances (SCUs) were regressed withineach recording against age for the typically developing sample, yieldinga normative model of development with respect to acoustic organizationof vocalizations. After the model had been developed, its coefficientswere used to calculate developmental scores for the autism andlanguage-delayed recordings. Growth in the developmental scores acrossage was found for the typically developing sample and thelanguage-delayed sample, but not for the autistic sample, whosedevelopmental scores were also in general considerably below those ofthe typically developing sample. FIGS. 24-29 show the results of theanalysis.

In block 1830 of FIG. 17, the data set related to the key child inquestion is compared to the trend lines of known subjects in order tomake a determination as to whether the individual is autistic, delayed,or normal. As shown in FIG. 30, Logistic Regression Analysis, was usedto model optimum classification of children as autistic or non-autisticbased on the 12 acoustic parameters. In the case of normally developingchildren, a high percentage of normal children were identified asnormal.

In FIG. 31, a number of tables are shown showing the accuracy of variousmethodologies of determining the likelihood of autism. Using LogisticRegression and an equal error rate (EER), the method had a high degreeof success while only delivering a small number of false positives. Forinstance, in the case where a probability of 0.98 was used, the systemand method determined that 93% of those subjects were considered normal,and only had a small error rate in determining that some normalindividuals were autistic. At the same time, only 12% of individualswere determined to be normal when they were really autistic, and 88% ofautistic individuals were correctly identified as autistic. The bottomrow of tables shows the alternative Linear Discriminant Analysis, andshows similar results.

Although the above system and method is described for application indetecting autism, it may be used in for a number of different diseasesand disorders related to speech. Through capturing informationconcerning trends in the population, processing the information todetermine trends, and comparing individuals to those trends, diseasesand disorders may be diagnosed. Generally, the model/trend creationfunctions according to the same principles described in FIG. 18. Bysegmenting the sound signal in block 1835 to reveal those soundsproduced by the subject intended to be studied and then furthersubdividing the sounds of the subject into at least those sounds thatare vocalizations and those sounds that are not in block 1840, the soundsignal to be studied can be pinpointed. Then through the acousticanalysis and development of transparent parameters in blocks 1845 and1850, the features of the sound signals can be revealed. From thesefeatures, compared to the prevalence of the disease or disorder in theindividuals studied, a trend or model can be created in block 1855 thatmay be used to compare new subjects in order to determine if they havethe disease or disorder. New subjects are processed according to FIG. 17in a similar fashion and ultimately compared to the trend determined inblock 1830. Furthermore, although the above description focuses onvocalization data, as the database of child recordings in a naturallanguage environment grows for children of very young (less than a year)ages, data concerning the cries of children may reveal trends that canallow for the detection of autism.

In an alternative embodiment, autism (and other diseases) may bedetected using either solely the above-described phone analysis inrelation to child language development or the above-described phoneanalysis in conjunction with transparent feature analysis. Usingfrequency of phones or a PCA (principal component analysis)dimension-reduced bi-phone analysis, human SLP assessment scores can bepredicted by an embodiment of the above-described system and method. Aphone-based feature used for AVA could be used for autism detection withthe rest of the system unchanged, including LDA (linear discriminantanalysis), logistic regression, etc. The addition of phone-based featureanalysis to acoustic transparent feature analysis could provideadditional resolution in respect to autism detection. Furthermore,although much of the analysis is focused on vocalizations as thedatabase of child recordings in a natural language environment grows forchildren of very young (less than a year) ages, data concerning thecries of children may reveal trends.

Phone-Based Autism Detection

In an example of an alternative embodiment, phone-based features areused to detect autism. Alternative methods of incorporating multiplerecordings for analysis of the language of a single child are alsoincluded. The method included incorporating multiple recordings for achild in a posterior probability space as opposed to merging multiplerecordings at the input feature space. These methods are specificallytargeted towards autism in this example; however, they may be utilizedfor the detection of other disorders and the analysis of speechaccording to any method described herein. In the present example,phone-based features yielded better results than the above explainedtransparent features. This was especially true for distinguishing autismfrom language delay.

There are basically two types of features: “transparent features” (seeabove discussion) and phone-based features that are used in the analysisof autism, and these features may be applied in the analysis of anydisorder or characteristic of an individual detectable through theanalysis of speech. Another possible analysis may include thecombination of both transparent and phone-based features. So “ft-12”represents “transparent feature”, the “ft” meaning transparent featureand the 12 meaning the number of transparent features (as discussed inthe previous embodiment); “biph-50” represents bi-phone-based featurewhich has 50 dimensions through PCA (principal component analysis). A“combined” analysis represents putting “ft-12” and “biph-50” together.

All three types of features, ft-12, biph-50, and combined could be“age-normalized”, i.e., based on the mean and the standard deviation ofa feature for each month-age group in Set-N, to remove the mean andscaling with the standard deviation: new_feature=(old_feature−mean)/std.

The method of incorporating multiple recordings from a single child mayvary; and in the present example, it was determined that using posteriorprobabilities was most effective considering the data used. Previously,age-normalized features from different recordings are averaged togetherto form a single feature vector for a child. Alternatively, as in thepresent example, each individual recording and its feature vector may beused to obtain posterior probability. The incorporation of multiplerecordings for a child may be done in the posterior probability space.The posterior probabilities from multiple recordings may be averagedtogether to obtain a single averaged posterior probability for a child.The average may be “geometric” or “arithmetic”.

A. Data Used

The data used in the present example is the same data described aboveand depicted in FIG. 23. This data includes three sets of children: 1)typically developing or normative children (represented by “N” or “n” inTable 1 below); 2) language-delayed children (represented by “D” or “d”in Table 1 below); and 3) autistic children (represented by “A” or “a”in Table 1 below). There are 328 children and 2678 recordings in Set-N,30 children and 290 recordings in Set-D, and 34 children and 225recordings in Set-A. All recordings are day-long (longer than 12 hours).A summary of the data is:

Set-A: autistic children; 34 children; 225 recordings

Set-D: delayed children; 30 children; 290 recordings

Set-N: typical children; 328 children; 2678 recordings

The three basic tasks are based on each pair of Set-N, D, A to see theclassification of each pair of them: 1) classification of autism fromdelay; 2) classification of delay from normal; and 3) classification ofautism from normal. For autism detection, the detection of autism fromnormative set and delayed set is the actual focus. Additionalresolution, even for autism versus non-autism (delayed+typical), isachievable in relation to the detail of separating autism from delay andseparating autism from typical set. Following is the summary of sixcases investigated (and reflected in Table 1):

a-d: of Set-A from Set-D, trained and tested with LOOCV on Set-A, D;

d-n: detection of Set-D from Set-N, trained and tested with LOOCV onSet-D, N.

a-n: detection of Set-A from Set-N, trained and tested with LOOCV onSet-A, N.

a-dn: detection of Set-A from Set-D and N, trained, tested with LOOCV onSet-A,D,N

a-dn_a-d: training is the same as “a-dn”, but just check the performanceof “a-d”.

a-dn_a-n: training is the same as “a-dn”, but just check the performanceof “a-n”.

B. Performance Measure

In the present example, performance of the system was tested using LOOCV(leave-one-out-cross-validation). LOOCV may be used for the validationof the detection of other disorders or classifications other thanautism, such as the many disorders and classifications discussedelsewhere in this disclosure.

As part of LOOCV validation, subjects are classified into two classes:class-c (the classification of the child being validated) and theothers, which could be called non-c class. Specifically, one child isleft out of the model each time, regardless of whether the child isassociated with one feature vector, which is some kind of combinationfrom multiple recordings, or if the child is associated with severalfeature vectors, which are from each corresponding recording for thatchild.

When a child is left out, all of its associated feature vector(s) areleft out during the training of a model with the rest of the data. Themodel then is applied to that child to obtain the posterior probabilityof being class-c given the feature vector(s) as the observation. Theprocess circulates through all children. In the end, each child willhave its posterior probability of being class-c.

A ROC curve (Receiver Operating Characteristic curve, which is a plot ofthe true positive rate against the false positive rate for the differentpossible cutpoints of the test) could be drawn based on posteriorprobabilities of all children. Equal-error-rate could be calculated atthe same time. Specifically, the procedure to draw ROC and to calculateequal-error-rate is as follows:

1. array_p=unique posterior probabilities sorted in ascending order

2. threshold_array=[array_p(1 . . . n−1)+array_p(2 . . . n)]/2, i.e.,middle-points between neighboring unique posterior probabilities

3. final_threshold_array=[0, threshold_array, 1], i.e., adding in 0 and1 as thresholds

4. For each threshold from 0 to 1, do the following:

with a specific threshold, detection decision could be made: any childis detected as class-c if its posterior probability is above thethreshold; otherwise, the child is detected as class non-c

detection error rate and false alarm rate for this threshold is:

detection_error_rate=number-of-class-c-children-mis-detected-as-non-c/number-of-class-c-children

false_alarm_rate=number-of-class-non-c-children-mis-detected-as-c/number-of-class-non-c-children

5. The curve of detection_rate (detection_rate=1−detection_error_rate)or detection_error_rate versus the posterior probability threshold couldbe drawn by connecting each point of (rate, threshold) obtained in step4.

Similarly, the curve of non-c_detection_rate (=1−false_alarm_rate) orfalse_alarm_rate versus the posterior probability threshold could bedrawn by connecting each point obtained in step 4.

6. The equal-error-rate-point is the intersection of two curvesmentioned in step 5. The calculation of the intersection point istrivial since the two curves are either monotonically increasing ordecreasing.

The equal-error-rate was used as a performance measure for comparison ofdifferent methods and different features used.

FIG. 34 shows ROC for a case “a-d” in Baseline with LDA method. FIG. 35shows ROC of a case “a-d” with biph-50 feature andgeometric-posterior-probability average to combine multiple recordingsof a key child.

C. Analysis Technique

In this example, a feature vector is converted into a posteriorprobability; although, explained in the context of autism detection,this technique may be applied to other analyses of speech to determinecharacteristics or disorders of an individual. Two modeling methods areused to perform the conversion: logistic regression and LDA (lineardiscriminant analysis).

Logistic Regression uses the following function to convert a featurevector into posterior probability:posterior_probability=1/(1+exp(A*feature_vector+b))where A is a linear model vector, * is inner product, and b is offsetparameter. Both A and b could be estimated using Maximum Likelihoodmethod with Newton-Raphson optimization algorithm.

LDA itself could not directly provide posterior probability. The purposeof LDA is to find a linear transform so that the Fisher-Ratio isoptimized in the output space of the linear transform or discriminationis optimized in the output space.

Once optimal LDA linear transform is determined, the data distributionof each class could be estimated under the assumption of Gaussian(Normal) distribution. With the provided a priori probability of eachclass, the posterior probability could be calculated:P(c|x)=P(c)*P(x|c)/P(x),P(x)=sum P(c)*P(x|c),where P(c|x) is the posterior probability of being as class-c givenobservation x; P(c) is a priori probability of class-c; and P(x|c) isthe data distribution of class-c.

Data distribution of P(x|c) may be obtained under the assumption ofGaussian distribution. The Maximum Likelihood solution is sample meanand sample variance.

As described above, equal-error-rates for the case of “a-d”, “d-n”, and“a-n” are provided. However, instead of manually adjusting the cutoffthreshold, which may not be precise and consistent, equal-error-ratesare obtained by automatic algorithm, which is more accurate and worksmore consistently. In addition, the performance for the case of “a-dn”,“a-dn_a-d”, and “a-dn_a-n” are added. The new results are in Table 1.

From the results of baseline system, we can see that LDA worksconsistently better than Logistic Regression.

The trial for the presently described example included:

A. The six detection cases mentioned above (and reflected in theDetection Cases column of Table 1)

B. The three types of features mentioned above (ft-12, biph-50, andcombined)

C. The three types of features in their raw values or age-normalizedvalues

D. Child level performance with the old way of combining multiplerecordings for a child by averaging the age-normalized feature together

E. Child level performance with the new way of averaging posteriorprobabilities of multiple recordings for a child. The average includes“geometric” and “arithmetic”.

D. Recording Level Performance

The experiments are based on leave-one-child-out mentioned above, i.e.,all associated recordings with one child are left out during thetraining phase of its model and then use that model for left outrecordings to obtain posterior probability for that child.

From Table 1, it is clear that, in the context of this example with theavailable data, the following can be observed:

1. Since Set-D (30) and Set-A (34) are limited in samples, one sample isabout 1/30=3%. Therefore, one data point's posterior (position) mighthave the influence of about 3% difference in equal-error-rate. Whenlooking at Table 1, this should be kept in mind.

2. The “ft-12” is done basically as described above in relation to thedetermination of autism according to transparent features.

3. The single recording performance is worse than that of child-level.In other words, the performance of child-level could be improved byusing multiple recordings for a child.

4. The geometric average of posterior probability for multirecordings ofa child is usually better than arithmetic average.

5. biph-50 is significantly better than ft-12, especially for delayedversus autistic. The superior of biph-50 over ft-12 is consistentthrough all cases.

6. The combination of ft-12 and biph-50 is slightly better than biph-50(not that much for d-a case, mainly for n-d and n-a cases). It seemsthat Set-N has a lot of samples and a wider range of ages, especiallyyoung age 2-15, and ft-12 is less sensitive to age, while biph-50 ismore sensitive to age for 2-15 or 2-10 which exist only in Set-N. Afterage normalization, the advantage of the combination of ft-12 and biph-50over biph-50 was minimal. Age normalization appears to help biph-50feature for “d-n” and “a-n” cases but not for “a-d” cases, which doesnot have kids below 10 months. Intuitively, it is possible that the veryyoung age group of Set-N have some sort of irregularity in the dataresulting in difficulties in discrimination for cases “a-n” and “d-n”.

7. The new way of combining posteriors of multiple recordings for achild is better than the old way of averaging features (including ft-12,biph-50, and their combination) that predict posterior for a child. For“a-d” cases, child level performance is even worse than recording levelperformance. For “a-dn_a-d” cases, child level performance is slightlybetter than recording level performance. This supports the fact that theamount of training data is important for generalization.

8. LDA is consistently better than Logistic Regression.

Of course, it is believed this analysis will hold true for additionaldata, but there is the possibility that it will not and the analysistechniques will have to be compared for any new set of data.

TABLE 1 Equal-Error-Rate (%) Comparison New Trials (all are LDA Method)Child-level Performance posterior- Recording- Baselineprobability-average level Detection logistic old geometric arithmeticperformance Cases Features regress LDA way raw age-n raw age-n raw age-na-d ft-12 40.00 35.29 36.67 33.33 30.00 33.33 32.35 33.79 35.11 biph-5032.35 17.65 20.59 20.00 20.59 24.00 23.56 combined 30.00 17.65 20.5914.71 23.33 22.22 25.78 d-n ft-12 23.33 16.67 16.67 23.78 20.00 25.0020.12 32.76 26.21 biph-50 11.89 10.67 6.67 13.33 6.71 24.14 19.31combined 10.00 8.84 6.67 13.72 7.93 23.94 17.24 a-n ft-12 11.77 8.828.84 11.77 5.88 11.77 8.82 19.72 16.00 biph-50 5.88 6.10 2.94 8.82 5.8816.00 11.20 combined 5.88 2.94 5.49 3.05 5.88 12.44 10.22 a-dn ft-1211.77 9.24 11.77 11.77 10.06 13.13 10.62 21.78 18.67 biph-50 9.22 8.827.26 8.82 8.94 17.33 13.33 combined 8.82 4.47 7.82 5.88 8.10 14.22 12.44a-dn_a-d ft-12 33.33 30.00 30.00 33.33 35.29 32.35 33.33 32.07 32.89biph-50 23.33 20.00 20.59 20.00 23.33 25.52 27.59 combined 20.00 20.0016.67 20.00 20.00 25.17 24.44 a-dn_a-n ft-12 11.62 8.82 8.54 11.77 5.8811.77 8.82 20.89 17.18 biph-50 8.82 8.23 5.88 8.82 8.82 16.06 12.00combined 5.88 2.94 5.88 5.79 5.88 12.89 11.56

Additionally, the posterior probability may be combined into theabove-described analysis techniques for determining the developmentalage of a key child; or it can be used in the detection of otherdisorders, diseases, or characteristics from the analysis of speech.

In one embodiment of a method of detecting autism, a party interested ina detecting autism in a child may request a test system be sent to them.In response, a test system may be sent to them by mail or other deliverymeans, or may be given to them by a doctor or medical professional. Thesystem includes the recording unit, instructions, and clothing for thesubject (the key child) to wear that is adapted to hold the recordingunit. The child is then recorded for the specified period and the systemis returned by mail or physically returned to a central processingreceiver. The central processing receiver, then retrieves the data fromthe system and processes the data. Reports are returned to the necessaryparties which may include the parents of the key child, the physician,other professionals, etc. This method may be implemented in a low costfashion since the key child or key child's guardian/parent is in effect“renting” the unit for a one time use. After usage the same unit may bereused for another subject who will pay the “rental” fee, collect theneeded data, return the unit, and receive the needed test results.

Development of a Child Model and Unsupervised Analysis

As discussed above, some embodiments use automatic speech recognition(ASR) systems designed for adults in order to identify phones for use indetermining a child's developmental level. One such ASR is the Sphinxdecoder. This decoder and others like it are based on a phone modeldeveloped from adult speech. Although the speech of children is similarto that of adults, an ASR designed for adults may not produce optimalphone detection for children. The adult ASR is based on adult speech.The data analyzed is child speech. Therefore, the data from which themodel was created may have limitations or inaccuracies when compared todisparate data, e.g., child speech. In order to eliminate data modelmismatch, a model created from the analysis of child speech may be used.

Traditionally, a speech model for children could be created by directlytraining and creating a speech model. This would resolve the data modelmismatch. This process would involve a professional listening to childrecordings and classifying the phone spoken by the child. However,labeling child speech is a very time consuming and error-prone task,because child speech usually is not well-pronounced and has largevariations. Therefore, supervised child speech modeling might bedifficult and costly.

Instead, in one embodiment, unsupervised clustering methods could beused for child speech modeling. This method, based on the statisticalcharacteristics of data, clusters similar child speech data together.This methodology may reduce the need for human classification of childspeech. Since the above methods are based on statistically comparing thedevelopment of a subject to a model for development of known subjects,the actual phones spoken may be excluded from the analysis. Instead,clusters of speech segments that may or may not represent actual phonesare developed, and the speech of a subject is compared to theseclusters.

One methodology of clustering is a K-means. A brief description ofK-means algorithm is given in the following:

-   -   1. For a given data set {x_(i)|i=1, . . . , n}, K-means        algorithm is trying to find k representative points {c_(i)|i=1,        . . . , k}, where k is smaller (or much smaller) than n. c_(i)        are cluster centroids or cluster means. This is why it is called        K-means.    -   2. Initializing c_(i). This could be done by randomly choosing        from a data set or by other methods.    -   3. For each data point x_(i), find the closest cluster by        measuring the distance to each cluster centroid, and label this        data point as that cluster.    -   4. For each cluster, pool all the data points labeled as this        cluster and calculate the mean of this cluster. Update the        cluster centroid with the new calculated mean.    -   5. Iterate step 3 and step 4 until some convergence criterion is        met (theoretically, the iteration is guaranteed to converge to        at least the local minimum of smallest overall data        “distortion”).

The obtained clusters of child speech are considered to resemble phones,and analysis is performed according to the above uni-phone or bi-phoneanalysis substituting the cluster model for the ASR adult model. Childspeech then could be decoded with cluster models (centroids) to find outthe cluster label sequence of child speech. This is much like thephone-decoding process using the adult-phone model. The cluster labelsequence, then, could be used in the same way as the phone sequence usedin AVA analysis.

FIG. 32 shows an illustration of K-means clusters (centroids). As shownin the figure, dots represent data points, stars represent cluster means(centroids), and black lines illustrate the boundaries in the featurespace among different clusters which are defined by cluster means(centroids). A K-means algorithm will automatically find optimal “stars”given “dots”. The “optimal” is in the sense of minimum distortion (atleast locally).

Table 2 below shows experimental results based on an unsupervised childmodel.

TABLE 2 Adult Model And Unsupervised Child Model Comparison Correlationbetween human SLP scores and machine scores (leave one out ModelingDetail cross-validation) Adult Model Uni-phone 0.718 (Sphinx) Bi-phonewith PCA 0.746 (50 feature after PCA) Unsupervised 64-cluster(uni-phone-way) 0.730 Child Model 64-cluster-for-above-15-month 0.744and 16-cluster-for-below-15 (uni-phone-way)

The above table shows essentially the same performance of unsupervisedmethod as the one using an adult phone model. This is a verification ofprevious analysis using an adult phone model. At the same time, thisalso shows the promise and potential of unsupervised method because itmay be more flexible in terms of number of clusters to choose, etc.Although particular numbers of clusters are shown, the optimal number ofclusters for a given data set may depend on the size of the data set andvarious numbers of clusters may be used.

Furthermore, cluster-based feature analysis can be used for autismdetection or the detection of other disorders/diseases. Again, thecombination of cluster-based feature, adult-phone-model-based feature,acoustic-transparent feature could be done towards autism detection.Currently, in the case of autism detection, transparent features areused in the analysis. Referring to FIG. 21, a table of acousticparameters is shown. The acoustic parameters shown are extracted fromrecordings. However, these acoustic parameters are based on real wordobservations and not clustering. In an alternative cluster-basedtransparent parameter analysis, clusters are developed in relation tothe characteristics of speech and sound. These characteristics mayinclude the pitch of the sound, the duration of the sound, the rhythm ofthe sound, the organization of the sound, etc. For instance, in the caseof sound duration, the acoustic parameters shown have definitions forshort, medium, long, and extra-long duration islands. Instead, thesedefinitions may be established by clustering of actual sound recordingsand will create a cluster model representative of the data collected.

In this way, the model developed may be finely tuned according tospecific age and any other characteristics that are known about thepopulation representing the recording data upon which the model isbased. On a most basic level, the characteristics of speech primarilyconsist of the pitch of the speech, the duration of the speech, andorganization of the speech. Clustering can be done according to any andall of these characteristics alone and in combination. Additional speechcharacteristics may include speech flow, loudness, intonation, andintensity of overtones. Speech flow includes the production speed ofutterances and the length of breaks in speaking. Loudness is the amountof energy associated with the speech. Intonation relates to rise andfall in pitch with respect to the speaker's mean vocal pitch. Overtonesinclude higher tones which accompany fundamental tones and are generallyfainter than the fundamental tone. All of these characteristics and morecan be used to form clusters.

Clustering allows for analysis in the absence of preconceived notionsabout the characteristics of speech and may reveal patterns previouslyunrecognized. As long as the sample collected is large enough(statistically speaking), the patterns revealed through clustering willhold true for a population and may be applied to any type of speechanalysis in terms of development, detection of disease and disorder(such as autism), and other characteristics of speech, such as emotion,the speaker's underlying motivations, veracity, for example.

Emotion/Stress Detection

It is theorized that the emotions expressed by parents and caregiversmay affect the language development of children. The above-describedmethods and systems lend themselves well to determining the effect ofemotion on child language development. One embodiment of a methodologyfor determining emotion in an utterance is shown in FIG. 33. Forpurposes of the analysis, it is assumed that one utterance contains onlyone type of emotion, or in stress detection case, is either stress orneutral (non-stress). When input utterance is received, emotion-relatedacoustic feature is extracted. Mel-frequency cepstral coefficient (MFCC)and Perceptual Minimum Variance Distortionless Response (PMVDR) may beused as the feature for emotion detection. Once the feature isextracted, the utterance is scored upon the feature in respect to aplurality of models representing emotions. The model having the maximumscore is selected, and the emotion associated with that model isidentified as the emotion status of the utterance. A Gaussian MixtureModel (GMM) may be used for the scoring, which is described above forsegmentation and segment-ID task. In the context of emotion detection,the detection of a stressed or non-stressed condition may be simplerthan specific emotion detection and, thus, may be more accurate. Thismethodology may be performed using the speech detection and analysissystem described herein.

In order to experiment with the described method and system and tooptimize the model size and feature size, emotion data is needed. A freeGerman emotion database was used, available via the Internet. Twentyfull-day natural home environment recordings from 20 different ordinaryAmerican families were processed according to the above-describedsegmentation and ID system, annotated the automatically detected adultutterances for stress and non-stress detection, and obtained about 900human confirmed stress/non-stress-labeled utterances for this purpose.The data set is called LENA-Emotion-Data-1. The described emotiondatabase is unique and valuable for emotion detection research anddevelopment in a natural home environment and how emotion may affectchild speech and language development. The system for speech collectiondescribed in the '520 Application allows for collection of speech in thenatural language environment, and processing techniques described aboveprovide for filtering and segmentation of the recorded sound signal.

With the German emotion database, MFCC, PMVDR and GMM, optimal modelsize and feature size were searched. For model size, with a fixed36-order-MFCC and its derivative feature (or delta feature, total72-dimenssion), optimal GMM size was searched. As shown in Table 3, 128Gaussians for each emotion GMM model gave the best detection rate forthe task of all emotion detection (64.57%) and stress-v.-non-stressdetection (89.83%). With the fixed 128 Gaussians per GMM model size,MFCC feature size was further optimized. As shown in Table 4, MFCCfeature size of 12 (MFCC+its-delta=24 dimension) gave the best detectionrate on the German database. PMVDR was also compared with MFCC foremotion detection task. The experiment result is shown in Table 5.

TABLE 3 Model size optimization, Detection Rate v. number of Gaussiansper model # Gaussians/model 8 16 32 64 128 256 ALL Emotions 56.44%56.81% 60.79% 64.22% 64.57% 60.69% Stress v. 84.37% 84.57% 88.13% 88.79%89.83% 88.67% Non-stress

TABLE 4 MFCC feature size for emotion detection on German databaseFeature Size 12 14 16 18 20 22 24 26 All Emotions 66.76 69.92 70.3969.53 70.11 72.92 73.36 69.69 Stress v. 88.88 91.09 90.94 90.32 90.5992.71 92.68 91.34 Non-stress

TABLE 5 Different Feature for Emotion Detection on German databaseMFCC(24) PMVDR(24) All Emotions 73.36 73.80 Stress v. Non-stress 92.6893.16

To incorporate more information about emotion in the feature used, thedimension of feature needs to be increased to include more relevantcharacteristics. This may be done by using higher orders of MFCC orPMVDR and including more context (or neighboring) feature frames tocover dynamics of speech which may be associated with emotion. However,increasing the feature dimension may not necessarily improve thedetection rate. The reason is that the increased feature dimension mayresult in the model size increase and thus intensify the conflictbetween model size and limited amount of training data. Althoughincreasing feature size may incorporate more useful information,increasing the feature size could also introduce some irrelevantfeatures or noise. This could make the modeling process even harder toconverge to relevant characteristics of input features. To resolve thisissue, Linear Discriminant Analysis (LDA) is used to reduce the featuredimension to reserve the most relevant information from high or veryhigh dimensional features. Alternatively, other forms of analysis thatcan reduce the dimensionality are used, including feature extraction andfeature selection techniques. A simple test in Table 6 showed that LDAhelps to reduce the feature dimension and model size and eventuallyimprove the emotion detection rate.

TABLE 6 Simple Test of LDA for emotion detection on German database12-dimension MFCC 6-dimension of LDA All Emotions 58.41 58.39 Stress v.Non-stress 84.72 85.30

The output dimension of standard LDA may be confined by the total numberof classes involved (actually the maximum number of output feature forstandard LDA is J−1 if there are J classes). For stress-v.-non-stressdetection, the standard LDA can only have one output feature, which maynot be good enough. To resolve this issue, sub-class LDA was proposed.For each class, different sub-classes (or clusters) could be obtainedusing, e.g., K-means algorithm which is described earlier. Since this isbasically an unsupervised method, each class can have as manysub-classes as needed. Once sub-classes are generated for each class,the total number of sub-class-pair between each class-pair could be verylarge, resulting in the number of LDA output virtually unconfined. Withthis method, experiments were done on German database. Table 7 shows thecomparative result, confirming that LDA improves the emotion detectionperformance.

TABLE 7 Sub-Class LDA Emotion Detection Result on German database24-dimension MFCC 34-dimension of LDA All Emotions 73.36 75.62 Stress v.Non-stress 92.68 94.82 24-dimension MFCC: The best MFCC result obtained.34-dimension of LDA: each class has 5 sub-classes, and 7 context frameswere used in LDA

The German database is acted emotion data. Infoture LENA-Emotion-Data-1comes from a real natural home environment in an unobtrusive way. Totest ideas and methods for emotion detection on InfotureLENA-Emotion-Data-1 may be interesting since the InfotureLENA-Emotion-Data-1 was collected in a natural language environment.Initially, the model trained with the German database was applied onLENA-Emotion-Data-1 for stress/non-stress detection. The detection rateis 51%, similar to random guessing. This is probably due to the mismatchbetween the LENA-Emotion-Data-1 and the model trained from the Germandatabase. To resolve this issue, models trained on LENA-Emotion-Data-1are directly tested on LENA data. However, to deal with the limitedamount of LENA data, leave-one-recording-out-cross-validation was usedto take advantage of labeled LENA-Emotion-Data-1 available, while thereis no single testing recording family involved in the training of itstesting model. This gives the results shown in Table 8, confirming thatthe current method is feasible for the real natural home environmentdata like LENA-Emotion-Data-1 for stress detection.

TABLE 8 Leave-one-recording-out-cross- validation on LENA-Emotion-Data-1Feature Used MFC-12 MFC-40 Stress Detection Rate 68.6% 70.5%

An indication as to the emotion of responses and interactions that thechild has may be valuable in gaining greater resolution into a child'slanguage development and how to further improve a child's naturallanguage environment. The present systems and methods are wellpositioned to perform such analysis due to their non-intrusive nature.

Tailoring of Analysis Techniques

A number of analysis techniques are mentioned herein to address thedetection of developmental age, autism, emotion, etc. Although theanalysis techniques expressed are believed to be the best availabletechniques for the determination of such characteristics, they are basedat least in part on the quality and quantity of data collected on whichthe analysis is based. Therefore, the individual techniques utilized atvarious stages of the analysis may be interchanged. For instance, LDAand Logistical Regression Analysis may be interchanged depending ontheir performance characteristics, as may methodologies of incorporatingmultiple recordings for a subject and choice of recording features used(transparent features vs. phone-based features).

In all cases of the above-described embodiments, the results of any ofthe transformations of data described may be realized by outputting theresults by transforming any physical or electronic medium available intoanother state or thing. Such output includes, but is not limited to,producing hardcopy (paper), sounds, visual display (as in the case ofmonitors, projectors, etc.), tactile display, changes in electronicmedium, etc. The foregoing description of the embodiments of theinventions has been presented only for the purpose of illustration anddescription and is not intended to be exhaustive or to limit theinventions to the precise forms disclosed. Numerous modifications andadaptations are apparent to those skilled in the art without departingfrom the spirit and scope of the inventions.

What is claimed is:
 1. A method comprising: capturing an audio recordingfrom a language environment of a key child; segmenting the audiorecording into a plurality of segments using a Minimum Duration GaussianMixture Model (MD-GMM) technique, the MD-GMM technique comprisingperforming a maximum log-likelihood analysis to generate the pluralityof segments having a minimum duration constraint; identifying a segmentID for each of the plurality of segments, the segment ID identifying asource for audio in the segment of the plurality of segments;identifying a plurality of key child segments from the plurality ofsegments, each of the plurality of key child segments having the keychild as the segment ID; estimating key child segment characteristicsbased in part on at least one of the plurality of key child segments,wherein the key child segment characteristics are estimated independentof contents of the plurality of key child segments, wherein the contentsare meanings of the plurality of key child segments; determining atleast one metric associated with the language environment using the keychild segment characteristics; and outputting the at least one metric.2. The method of claim 1, further comprising: identifying a plurality ofadult segments from the plurality of segments, each of the plurality ofadult segments having an adult as the segment ID; and estimating adultsegment characteristics based in part on at least one of a plurality ofadult segments, wherein the adult segment characteristics are estimatedindependent of contents of the plurality of adult segments, whereindetermining the at least one metric associated with the languageenvironment comprises using the adult segment characteristics.
 3. Themethod of claim 2, wherein adult segment characteristics comprise atleast one of: a word count; a duration of speech; a vocalization count;or a parentese count.
 4. The method of claim 2, wherein the at least onemetric comprises at least one of: a quantity of key child vocalizationsin a pre-set time period; a quantity of conversational turns, whereinthe conversational turns comprise a sound from one of the adult or thekey child, and a response to the sound from the other one of the adultor the key child; or a quantity of adult words directed to the key childin a pre-set time period.
 5. The method of claim 1, further comprising:performing a first segmentation using a first MD-GMM of the MD-GMMtechnique, the first MD-GMM comprising a plurality of models; andgenerating a second MD-GMM of the MD-GMM technique by modifying at leastone of the plurality of models, wherein: segmenting the audio recordinginto the plurality of segments comprises using the second MD-GMM; andidentifying the segment ID comprises using the second MD-GMM.
 6. Themethod of claim 5, wherein: the plurality of models comprise a key childmodel, an electronic device model, and an adult model; the key childmodel comprises criteria associated with sounds from a child; theelectronic device model comprises criteria associated with sounds froman electronic device; and the adult model comprises criteria associatedwith sounds from adults.
 7. The method of claim 6, wherein generatingthe second MD-GMM comprises at least one of: modifying the key childmodel using an age-dependent key child model, wherein the age-dependentkey child model comprises criteria associated with sounds from childrenof a plurality of ages; modifying the electronic device model; modifyingat least one of the key child model or the adult model using aloudness/clearness detection model, wherein the loudness/clearnessdetection model comprises a Likelihood Ratio Test; or modifying at leastone of the key child model or the adult model using a parentese model,wherein the parentese model comprises complexity levels associated withsounds of adults.
 8. The method of claim 1, further comprising:classifying each of the plurality of key child segments into one of:vocalizations; cries; vegetative sounds; or fixed signal sounds, whereinthe key child segment characteristics are estimated using at least oneof the plurality of key child segments classified into at least one ofvocalizations or cries.
 9. The method of claim 8, wherein classifyingeach of the plurality of key child segments comprises using at least oneof rule-based analysis or statistical processing.
 10. The method ofclaim 1, wherein the key child segment characteristics comprises atleast one of: a duration of cries; a quantity of squeals; a quantity ofgrowls; a presence of canonical syllables; a quantity of canonicalsyllables; a presence of repetitive babbles; a quantity of repetitivebabbles; a presence of protophones; a quantity of protophones; aduration of protophones; a presence of phoneme-like sounds; a quantityof phoneme-like sounds; a duration of phoneme-like sounds; a presence ofphonemes; a quantity of phonemes; a duration of phonemes; a word count;or a vocalization count.
 11. The method of claim 1, wherein: identifyingthe segment ID for each of the plurality of segments comprises using theMD-GMM technique, the MD-GMM technique further comprising correlating amaximum score for each of the plurality of segments to the source forthe audio in the segment based on an association of the source with themaximum score.
 12. A method comprising: capturing an audio recordingfrom a language environment of a key child; segmenting the audiorecording into a plurality of segments using a Minimum Duration GaussianMixture Model (MD-GMM) technique, the MD-GMM technique comprisingperforming a maximum log-likelihood analysis to generate the pluralityof segments having a minimum duration constraint; identifying a segmentID for each of the plurality of segments, the segment ID identifying asource for audio in the segment of the plurality of segments, whereinthe identifying the segment ID comprises comparing the plurality ofsegments to a plurality of models, wherein a model of the plurality ofmodels includes a key child model and the identifying the segment IDincludes identifying a plurality of key child segments from theplurality of segments; estimating key child segment characteristicsbased in part on at least one of the plurality of key child segments,wherein the key child segment characteristics are estimated independentof contents of the plurality of key child segments, wherein the contentsare meanings of the plurality of key child segments; determining atleast one metric associated with the language environment using the keychild segment characteristics; and outputting the at least one metric.13. The method of claim 12, wherein the plurality of models furthercomprise models for other children, male adults, female adults, noise,and TV noise.
 14. The method of claim 12, wherein the plurality ofmodels further comprise: an adult model that includes characteristics ofsounds from an adult; an electronic device model that includescharacteristics of sounds from an electronic device; a noise model thatincludes characteristics of sounds attributable to noise; an other childmodel that includes characteristics of sounds from a child other thanthe key child; a parentese model that includes complexity level speechcriteria of adult sounds; an age-dependent key child model that includescharacteristics of sounds from children of a plurality of ages; and aloudness/-clearness detection model that includes characteristics ofsounds directed to a key child.
 15. The method of claim 12, whereinsegmenting the audio recording into the plurality of segments using theMinimum Duration Gaussian Mixture Model (MD-GMM) technique comprisessegmenting the audio recording into the plurality of segments using amaximum likelihood analysis.
 16. The method of claim 12, wherein:identifying the segment ID for each of the plurality of segmentscomprises using the MD-GMM technique, the MD-GMM technique furthercomprising correlating a maximum score for each of the plurality ofsegments to the source for the audio in the segment based on anassociation of the source with the maximum score.
 17. A methodcomprising: capturing an audio recording from a language environment ofa key child; using a Minimum Duration Gaussian Mixture Model (MD-GMM)technique to simultaneously segment the audio recording and identify asegment ID for each of a plurality of segments segmented from the audiorecording, the segment ID identifying a source for audio in the segmentof the plurality of segments, wherein the identifying includes comparingthe plurality of segments to a plurality of models, the MD-GMM techniquecomprising generating the plurality of segments having a minimumduration constraint and correlating a maximum score for each of theplurality of segments to the source for the audio in the segment basedon an association of the source with the maximum score; determining atleast one metric associated with the language environment based on theplurality of segments that have been identified; and outputting the atleast one metric.
 18. The method of claim 17, wherein the MD-GMMtechnique comprises a maximum likelihood analysis.
 19. The method ofclaim 17, wherein: a model of the plurality of models includes a keychild model; and using the Minimum Duration Gaussian Mixture Model(MD-GMM) technique comprises identifying a plurality of key childsegments from the plurality of segments.
 20. The method of claim 19,wherein the determining the at least one metric comprises: using keychild segment characteristics to determine the at least one metric. 21.The method of claim 20, further comprising: estimating the key childsegment characteristics based in part on at least one of the pluralityof key child segments, wherein: the key child segment characteristicsare estimated independent of contents of the plurality of key childsegments; and the contents are meanings of the plurality of key childsegments.